font
Filter
×
2026
Tuyen, Nguyen Tan Viet; Georgara, Athina; Singh, Lokesh; Deshmukh, Jayati; Davey, Sean; Tisdale, Paul N.; Ramchurn, Sarvapali
Supporting ?Stop and Watch? in elderly care with socially assistive robots: insights from a participatory design workshop Proceedings Article
In: Baillie, Lynne; Smart, William D.; Graaf, Maartje De; Gombolay, Matthew; Torre, Ilaria (Ed.): Companion Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction, HRI Companion 2026, pp. 302–306, ACM Press, 2026.
Abstract | Links | BibTeX | Tags: assistive living technologies, elderly care, featured_publication, socially assistive robots
@inproceedings{soton511593,
title = {Supporting ?Stop and Watch? in elderly care with socially assistive robots: insights from a participatory design workshop},
author = {Nguyen Tan Viet Tuyen and Athina Georgara and Lokesh Singh and Jayati Deshmukh and Sean Davey and Paul N. Tisdale and Sarvapali Ramchurn},
editor = {Lynne Baillie and William D. Smart and Maartje De Graaf and Matthew Gombolay and Ilaria Torre},
url = {https://eprints.soton.ac.uk/511593/},
year = {2026},
date = {2026-03-01},
urldate = {2026-03-01},
booktitle = {Companion Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction, HRI Companion 2026},
pages = {302–306},
publisher = {ACM Press},
abstract = {ensuremath<pensuremath>?Stop and Watch? is an early-warning tool adopted by the UK NHS and is widely used in elderly care settings. The tool helps caregivers to recognise abnormal changes in residents? health. Despite its clinical value, the process remains highly manual, workload-intensive, and vulnerable to missed observations, particularly in environments facing staff shortages, frequent staff rotations, and increasing care demands. We argue that AI-based systems such as Socially Assistive Robots (SARs) and Assistive Living Technologies (ALTs) offer promising avenues for supporting and enhancing the ?Stop and Watch? tool. However, designing such systems requires a multidisciplinary effort to establish a comprehensive understanding of current practices and the priorities and concerns of all relevant stakeholders. This paper presents insights from a participatory design workshop held in a care home in the UK to explore how SARs and ALTs could meaningfully support the ?Stop and Watch? tool, understand stakeholders? expectations, perceived benefits, and concerns regarding deployment in this sensitive context.ensuremath</pensuremath>},
keywords = {assistive living technologies, elderly care, featured_publication, socially assistive robots},
pubstate = {published},
tppubtype = {inproceedings}
}
ensuremath<pensuremath>?Stop and Watch? is an early-warning tool adopted by the UK NHS and is widely used in elderly care settings. The tool helps caregivers to recognise abnormal changes in residents? health. Despite its clinical value, the process remains highly manual, workload-intensive, and vulnerable to missed observations, particularly in environments facing staff shortages, frequent staff rotations, and increasing care demands. We argue that AI-based systems such as Socially Assistive Robots (SARs) and Assistive Living Technologies (ALTs) offer promising avenues for supporting and enhancing the ?Stop and Watch? tool. However, designing such systems requires a multidisciplinary effort to establish a comprehensive understanding of current practices and the priorities and concerns of all relevant stakeholders. This paper presents insights from a participatory design workshop held in a care home in the UK to explore how SARs and ALTs could meaningfully support the ?Stop and Watch? tool, understand stakeholders? expectations, perceived benefits, and concerns regarding deployment in this sensitive context.ensuremath</pensuremath>
2025
Ramchurn, Gopal; Owen, Aled Lloyd; Ao, Shuang; Barnard, Pepita; Bergin, Aislinn Gomez; Parisio, Isabela; Valoor, Adarsh; Waheed, Maria; Holter, Carolyn Ten; Portillo, Virginia; Procter, Rob; Batterham, Paul; Downer, John; Winter, Peter; Krook, Joshua; Blockx, Jan; Braunschweig, Bertrand; Quintero, Karla; Poretschkin, Maximilian; Becker, Daniel; Campbell-Ratcliffe, Emily; Hawes, Ben; Shaw, Patricia; Patel, Reema; Tewari, Ashish; Thomas, Alec; Duncan, Paul; Gillings, Eliot
Frameworks and Toolkits for Assuring Responsible AI Book Section
In: Responsible Ai UK, University of Southampton, 2025.
Abstract | Links | BibTeX | Tags: featured_publication
@incollection{soton506057,
title = {Frameworks and Toolkits for Assuring Responsible AI},
author = {Gopal Ramchurn and Aled Lloyd Owen and Shuang Ao and Pepita Barnard and Aislinn Gomez Bergin and Isabela Parisio and Adarsh Valoor and Maria Waheed and Carolyn Ten Holter and Virginia Portillo and Rob Procter and Paul Batterham and John Downer and Peter Winter and Joshua Krook and Jan Blockx and Bertrand Braunschweig and Karla Quintero and Maximilian Poretschkin and Daniel Becker and Emily Campbell-Ratcliffe and Ben Hawes and Patricia Shaw and Reema Patel and Ashish Tewari and Alec Thomas and Paul Duncan and Eliot Gillings},
url = {https://eprints.soton.ac.uk/506057/},
year = {2025},
date = {2025-10-01},
urldate = {2025-10-01},
booktitle = {Responsible Ai UK},
publisher = {University of Southampton},
abstract = {This report explores experiences of using tools for assuring responsible practice in the build, deployment, and governance of AI systems, in particular frameworks and toolkits developed and published by RAi UK-funded projects and by other organisations. We examine evidence of how and why these tools are used, and of their usefulness and their limitations. We look for lessons that could improve AI assurance in the future, including potentially using AI-based tools to support AI assurance. The aim is to identify priority areas and key questions for RAi UK and other researchers and organisations to explore further, with the goal of improving the fit between the supply of, and demand for, tools that support organisational assurance of responsible AI .Responsible Ai UK and our partner for this event, Confiance.ai, brought people together from across government, public services, and industry, as well as project teams that develop support for responsible AI. The aim was to clarify how people should approach the development and use of toolkits and/or frameworks in practice. We wanted to synthesise findings from our research across the programme and to find and address any gaps. The workshop took place online on Thursday 10 April 2025.},
keywords = {featured_publication},
pubstate = {published},
tppubtype = {incollection}
}
This report explores experiences of using tools for assuring responsible practice in the build, deployment, and governance of AI systems, in particular frameworks and toolkits developed and published by RAi UK-funded projects and by other organisations. We examine evidence of how and why these tools are used, and of their usefulness and their limitations. We look for lessons that could improve AI assurance in the future, including potentially using AI-based tools to support AI assurance. The aim is to identify priority areas and key questions for RAi UK and other researchers and organisations to explore further, with the goal of improving the fit between the supply of, and demand for, tools that support organisational assurance of responsible AI .Responsible Ai UK and our partner for this event, Confiance.ai, brought people together from across government, public services, and industry, as well as project teams that develop support for responsible AI. The aim was to clarify how people should approach the development and use of toolkits and/or frameworks in practice. We wanted to synthesise findings from our research across the programme and to find and address any gaps. The workshop took place online on Thursday 10 April 2025.
2023
Krook, Joshua; McAuley, Derek; Anderson, Stuart; Downer, John; Winter, Peter; Ramchurn, Sarvapali D
AI Foundation Models: initial review, CMA Consultation, TAS Hub Response Miscellaneous
2023.
Links | BibTeX | Tags: Artificial Intelligence, Competition policy, featured_publication, Foundation Models, Large Language Models, markets
@misc{soton477553,
title = {AI Foundation Models: initial review, CMA Consultation, TAS Hub Response},
author = {Joshua Krook and Derek McAuley and Stuart Anderson and John Downer and Peter Winter and Sarvapali D Ramchurn},
url = {https://eprints.soton.ac.uk/477553/},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
publisher = {University of Southampton},
keywords = {Artificial Intelligence, Competition policy, featured_publication, Foundation Models, Large Language Models, markets},
pubstate = {published},
tppubtype = {misc}
}
Abioye, Ayodeji; Naiseh, Mohammad; Hunt, William; Clark, Jediah R; Ramchurn, Sarvapali D; Soorati, Mohammad
The effect of data visualisation quality and task density on human-swarm interaction Proceedings Article
In: Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), IEEE, 2023.
Abstract | Links | BibTeX | Tags: featured_publication
@inproceedings{soton479970,
title = {The effect of data visualisation quality and task density on human-swarm interaction},
author = {Ayodeji Abioye and Mohammad Naiseh and William Hunt and Jediah R Clark and Sarvapali D Ramchurn and Mohammad Soorati},
url = {https://eprints.soton.ac.uk/479970/},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
booktitle = {Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
publisher = {IEEE},
abstract = {Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.},
keywords = {featured_publication},
pubstate = {published},
tppubtype = {inproceedings}
}
Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.
Worrawichaipat, Phuriwat; Gerding, Enrico; Kaparias, Ioannis; Ramchurn, Sarvapali
Multi-agent signal-less intersection management with dynamic platoon formation Proceedings Article
In: 22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23), pp. 1542–1550, 2023.
Links | BibTeX | Tags: featured_publication
@inproceedings{soton478647,
title = {Multi-agent signal-less intersection management with dynamic platoon formation},
author = {Phuriwat Worrawichaipat and Enrico Gerding and Ioannis Kaparias and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/478647/},
year = {2023},
date = {2023-05-01},
urldate = {2023-05-01},
booktitle = {22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23)},
pages = {1542–1550},
keywords = {featured_publication},
pubstate = {published},
tppubtype = {inproceedings}
}
2016
Ramchurn, Sarvapali; Simpson, Edwin; Fischer, Joel; Huynh, Trung Dong; Ikuno, Yuki; Reece, Steven; Jiang, Wenchao; Wu, Feng; Flann, Jack; Roberts, S. J.; Moreau, Luc; Rodden, T.; Jennings, N. R.
A Disaster Response System based on Human-Agent Collectives Journal Article
In: Journal of Artificial Intelligence Research, vol. 57, pp. 661-708, 2016.
Abstract | Links | BibTeX | Tags: featured_publication
@article{eps374070b,
title = {A Disaster Response System based on Human-Agent Collectives},
author = {Sarvapali Ramchurn and Edwin Simpson and Joel Fischer and Trung Dong Huynh and Yuki Ikuno and Steven Reece and Wenchao Jiang and Feng Wu and Jack Flann and S. J. Roberts and Luc Moreau and T. Rodden and N. R. Jennings},
url = {http://www.jair.org/media/5098/live-5098-9699-jair.pdf},
year = {2016},
date = {2016-12-01},
urldate = {2016-12-01},
journal = {Journal of Artificial Intelligence Research},
volume = {57},
pages = {661-708},
abstract = {Major natural or man-made disasters such as Hurricane Katrina or the 9/11 terror attacks pose significant challenges for emergency responders. First, they have to develop an understanding of the unfolding event either using their own resources or through third-parties such as the local population and agencies. Second, based on the information gathered, they need to deploy their teams in a flexible manner, ensuring that each team performs tasks in The most effective way. Third, given the dynamic nature of a disaster space, and the uncertainties involved in performing rescue missions, information about the disaster space and the actors within it needs to be managed to ensure that responders are always acting on up-to-date and trusted information. Against this background, this paper proposes a novel disaster response system called HAC-ER. Thus HAC-ER interweaves humans and agents, both robotic and software, in social relationships that augment their individual and collective capabilities. To design HAC-ER, we involved end-users including both experts and volunteers in a several participatory design workshops, lab studies, and field trials of increasingly advanced prototypes of individual components of HAC-ER as well as the overall system. This process generated a number of new quantitative and qualitative results but also raised a number of new research questions. HAC-ER thus demonstrates how such Human-Agent Collectives (HACs) can address key challenges in disaster response. Specifically, we show how HAC-ER utilises crowdsourcing combined with machine learning to obtain most important situational awareness from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments, as well as task planning for responders on the ground. Finally, HAC-ER incorporates an infrastructure and the associated intelligence for tracking and utilising the provenance of information shared across the entire system to ensure its accountability. We individually validate each of these elements of HAC-ER and show how they perform against standard (non-HAC) baselines and also elaborate on the evaluation of the overall system.},
keywords = {featured_publication},
pubstate = {published},
tppubtype = {article}
}
Major natural or man-made disasters such as Hurricane Katrina or the 9/11 terror attacks pose significant challenges for emergency responders. First, they have to develop an understanding of the unfolding event either using their own resources or through third-parties such as the local population and agencies. Second, based on the information gathered, they need to deploy their teams in a flexible manner, ensuring that each team performs tasks in The most effective way. Third, given the dynamic nature of a disaster space, and the uncertainties involved in performing rescue missions, information about the disaster space and the actors within it needs to be managed to ensure that responders are always acting on up-to-date and trusted information. Against this background, this paper proposes a novel disaster response system called HAC-ER. Thus HAC-ER interweaves humans and agents, both robotic and software, in social relationships that augment their individual and collective capabilities. To design HAC-ER, we involved end-users including both experts and volunteers in a several participatory design workshops, lab studies, and field trials of increasingly advanced prototypes of individual components of HAC-ER as well as the overall system. This process generated a number of new quantitative and qualitative results but also raised a number of new research questions. HAC-ER thus demonstrates how such Human-Agent Collectives (HACs) can address key challenges in disaster response. Specifically, we show how HAC-ER utilises crowdsourcing combined with machine learning to obtain most important situational awareness from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments, as well as task planning for responders on the ground. Finally, HAC-ER incorporates an infrastructure and the associated intelligence for tracking and utilising the provenance of information shared across the entire system to ensure its accountability. We individually validate each of these elements of HAC-ER and show how they perform against standard (non-HAC) baselines and also elaborate on the evaluation of the overall system.
1.
Tuyen, Nguyen Tan Viet; Georgara, Athina; Singh, Lokesh; Deshmukh, Jayati; Davey, Sean; Tisdale, Paul N.; Ramchurn, Sarvapali
Supporting ?Stop and Watch? in elderly care with socially assistive robots: insights from a participatory design workshop Proceedings Article
In: Baillie, Lynne; Smart, William D.; Graaf, Maartje De; Gombolay, Matthew; Torre, Ilaria (Ed.): Companion Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction, HRI Companion 2026, pp. 302–306, ACM Press, 2026.
@inproceedings{soton511593,
title = {Supporting ?Stop and Watch? in elderly care with socially assistive robots: insights from a participatory design workshop},
author = {Nguyen Tan Viet Tuyen and Athina Georgara and Lokesh Singh and Jayati Deshmukh and Sean Davey and Paul N. Tisdale and Sarvapali Ramchurn},
editor = {Lynne Baillie and William D. Smart and Maartje De Graaf and Matthew Gombolay and Ilaria Torre},
url = {https://eprints.soton.ac.uk/511593/},
year = {2026},
date = {2026-03-01},
urldate = {2026-03-01},
booktitle = {Companion Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction, HRI Companion 2026},
pages = {302–306},
publisher = {ACM Press},
abstract = {ensuremath<pensuremath>?Stop and Watch? is an early-warning tool adopted by the UK NHS and is widely used in elderly care settings. The tool helps caregivers to recognise abnormal changes in residents? health. Despite its clinical value, the process remains highly manual, workload-intensive, and vulnerable to missed observations, particularly in environments facing staff shortages, frequent staff rotations, and increasing care demands. We argue that AI-based systems such as Socially Assistive Robots (SARs) and Assistive Living Technologies (ALTs) offer promising avenues for supporting and enhancing the ?Stop and Watch? tool. However, designing such systems requires a multidisciplinary effort to establish a comprehensive understanding of current practices and the priorities and concerns of all relevant stakeholders. This paper presents insights from a participatory design workshop held in a care home in the UK to explore how SARs and ALTs could meaningfully support the ?Stop and Watch? tool, understand stakeholders? expectations, perceived benefits, and concerns regarding deployment in this sensitive context.ensuremath</pensuremath>},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
ensuremath<pensuremath>?Stop and Watch? is an early-warning tool adopted by the UK NHS and is widely used in elderly care settings. The tool helps caregivers to recognise abnormal changes in residents? health. Despite its clinical value, the process remains highly manual, workload-intensive, and vulnerable to missed observations, particularly in environments facing staff shortages, frequent staff rotations, and increasing care demands. We argue that AI-based systems such as Socially Assistive Robots (SARs) and Assistive Living Technologies (ALTs) offer promising avenues for supporting and enhancing the ?Stop and Watch? tool. However, designing such systems requires a multidisciplinary effort to establish a comprehensive understanding of current practices and the priorities and concerns of all relevant stakeholders. This paper presents insights from a participatory design workshop held in a care home in the UK to explore how SARs and ALTs could meaningfully support the ?Stop and Watch? tool, understand stakeholders? expectations, perceived benefits, and concerns regarding deployment in this sensitive context.ensuremath</pensuremath>
2.
Ramchurn, Gopal; Owen, Aled Lloyd; Ao, Shuang; Barnard, Pepita; Bergin, Aislinn Gomez; Parisio, Isabela; Valoor, Adarsh; Waheed, Maria; Holter, Carolyn Ten; Portillo, Virginia; Procter, Rob; Batterham, Paul; Downer, John; Winter, Peter; Krook, Joshua; Blockx, Jan; Braunschweig, Bertrand; Quintero, Karla; Poretschkin, Maximilian; Becker, Daniel; Campbell-Ratcliffe, Emily; Hawes, Ben; Shaw, Patricia; Patel, Reema; Tewari, Ashish; Thomas, Alec; Duncan, Paul; Gillings, Eliot
Frameworks and Toolkits for Assuring Responsible AI Book Section
In: Responsible Ai UK, University of Southampton, 2025.
@incollection{soton506057,
title = {Frameworks and Toolkits for Assuring Responsible AI},
author = {Gopal Ramchurn and Aled Lloyd Owen and Shuang Ao and Pepita Barnard and Aislinn Gomez Bergin and Isabela Parisio and Adarsh Valoor and Maria Waheed and Carolyn Ten Holter and Virginia Portillo and Rob Procter and Paul Batterham and John Downer and Peter Winter and Joshua Krook and Jan Blockx and Bertrand Braunschweig and Karla Quintero and Maximilian Poretschkin and Daniel Becker and Emily Campbell-Ratcliffe and Ben Hawes and Patricia Shaw and Reema Patel and Ashish Tewari and Alec Thomas and Paul Duncan and Eliot Gillings},
url = {https://eprints.soton.ac.uk/506057/},
year = {2025},
date = {2025-10-01},
urldate = {2025-10-01},
booktitle = {Responsible Ai UK},
publisher = {University of Southampton},
abstract = {This report explores experiences of using tools for assuring responsible practice in the build, deployment, and governance of AI systems, in particular frameworks and toolkits developed and published by RAi UK-funded projects and by other organisations. We examine evidence of how and why these tools are used, and of their usefulness and their limitations. We look for lessons that could improve AI assurance in the future, including potentially using AI-based tools to support AI assurance. The aim is to identify priority areas and key questions for RAi UK and other researchers and organisations to explore further, with the goal of improving the fit between the supply of, and demand for, tools that support organisational assurance of responsible AI .Responsible Ai UK and our partner for this event, Confiance.ai, brought people together from across government, public services, and industry, as well as project teams that develop support for responsible AI. The aim was to clarify how people should approach the development and use of toolkits and/or frameworks in practice. We wanted to synthesise findings from our research across the programme and to find and address any gaps. The workshop took place online on Thursday 10 April 2025.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
This report explores experiences of using tools for assuring responsible practice in the build, deployment, and governance of AI systems, in particular frameworks and toolkits developed and published by RAi UK-funded projects and by other organisations. We examine evidence of how and why these tools are used, and of their usefulness and their limitations. We look for lessons that could improve AI assurance in the future, including potentially using AI-based tools to support AI assurance. The aim is to identify priority areas and key questions for RAi UK and other researchers and organisations to explore further, with the goal of improving the fit between the supply of, and demand for, tools that support organisational assurance of responsible AI .Responsible Ai UK and our partner for this event, Confiance.ai, brought people together from across government, public services, and industry, as well as project teams that develop support for responsible AI. The aim was to clarify how people should approach the development and use of toolkits and/or frameworks in practice. We wanted to synthesise findings from our research across the programme and to find and address any gaps. The workshop took place online on Thursday 10 April 2025.
3.
Krook, Joshua; McAuley, Derek; Anderson, Stuart; Downer, John; Winter, Peter; Ramchurn, Sarvapali D
AI Foundation Models: initial review, CMA Consultation, TAS Hub Response Miscellaneous
2023.
@misc{soton477553,
title = {AI Foundation Models: initial review, CMA Consultation, TAS Hub Response},
author = {Joshua Krook and Derek McAuley and Stuart Anderson and John Downer and Peter Winter and Sarvapali D Ramchurn},
url = {https://eprints.soton.ac.uk/477553/},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
publisher = {University of Southampton},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
4.
Abioye, Ayodeji; Naiseh, Mohammad; Hunt, William; Clark, Jediah R; Ramchurn, Sarvapali D; Soorati, Mohammad
The effect of data visualisation quality and task density on human-swarm interaction Proceedings Article
In: Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), IEEE, 2023.
@inproceedings{soton479970,
title = {The effect of data visualisation quality and task density on human-swarm interaction},
author = {Ayodeji Abioye and Mohammad Naiseh and William Hunt and Jediah R Clark and Sarvapali D Ramchurn and Mohammad Soorati},
url = {https://eprints.soton.ac.uk/479970/},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
booktitle = {Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
publisher = {IEEE},
abstract = {Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.
5.
Worrawichaipat, Phuriwat; Gerding, Enrico; Kaparias, Ioannis; Ramchurn, Sarvapali
Multi-agent signal-less intersection management with dynamic platoon formation Proceedings Article
In: 22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23), pp. 1542–1550, 2023.
@inproceedings{soton478647,
title = {Multi-agent signal-less intersection management with dynamic platoon formation},
author = {Phuriwat Worrawichaipat and Enrico Gerding and Ioannis Kaparias and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/478647/},
year = {2023},
date = {2023-05-01},
urldate = {2023-05-01},
booktitle = {22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23)},
pages = {1542–1550},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
6.
Ramchurn, Sarvapali; Simpson, Edwin; Fischer, Joel; Huynh, Trung Dong; Ikuno, Yuki; Reece, Steven; Jiang, Wenchao; Wu, Feng; Flann, Jack; Roberts, S. J.; Moreau, Luc; Rodden, T.; Jennings, N. R.
A Disaster Response System based on Human-Agent Collectives Journal Article
In: Journal of Artificial Intelligence Research, vol. 57, pp. 661-708, 2016.
@article{eps374070b,
title = {A Disaster Response System based on Human-Agent Collectives},
author = {Sarvapali Ramchurn and Edwin Simpson and Joel Fischer and Trung Dong Huynh and Yuki Ikuno and Steven Reece and Wenchao Jiang and Feng Wu and Jack Flann and S. J. Roberts and Luc Moreau and T. Rodden and N. R. Jennings},
url = {http://www.jair.org/media/5098/live-5098-9699-jair.pdf},
year = {2016},
date = {2016-12-01},
urldate = {2016-12-01},
journal = {Journal of Artificial Intelligence Research},
volume = {57},
pages = {661-708},
abstract = {Major natural or man-made disasters such as Hurricane Katrina or the 9/11 terror attacks pose significant challenges for emergency responders. First, they have to develop an understanding of the unfolding event either using their own resources or through third-parties such as the local population and agencies. Second, based on the information gathered, they need to deploy their teams in a flexible manner, ensuring that each team performs tasks in The most effective way. Third, given the dynamic nature of a disaster space, and the uncertainties involved in performing rescue missions, information about the disaster space and the actors within it needs to be managed to ensure that responders are always acting on up-to-date and trusted information. Against this background, this paper proposes a novel disaster response system called HAC-ER. Thus HAC-ER interweaves humans and agents, both robotic and software, in social relationships that augment their individual and collective capabilities. To design HAC-ER, we involved end-users including both experts and volunteers in a several participatory design workshops, lab studies, and field trials of increasingly advanced prototypes of individual components of HAC-ER as well as the overall system. This process generated a number of new quantitative and qualitative results but also raised a number of new research questions. HAC-ER thus demonstrates how such Human-Agent Collectives (HACs) can address key challenges in disaster response. Specifically, we show how HAC-ER utilises crowdsourcing combined with machine learning to obtain most important situational awareness from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments, as well as task planning for responders on the ground. Finally, HAC-ER incorporates an infrastructure and the associated intelligence for tracking and utilising the provenance of information shared across the entire system to ensure its accountability. We individually validate each of these elements of HAC-ER and show how they perform against standard (non-HAC) baselines and also elaborate on the evaluation of the overall system.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Major natural or man-made disasters such as Hurricane Katrina or the 9/11 terror attacks pose significant challenges for emergency responders. First, they have to develop an understanding of the unfolding event either using their own resources or through third-parties such as the local population and agencies. Second, based on the information gathered, they need to deploy their teams in a flexible manner, ensuring that each team performs tasks in The most effective way. Third, given the dynamic nature of a disaster space, and the uncertainties involved in performing rescue missions, information about the disaster space and the actors within it needs to be managed to ensure that responders are always acting on up-to-date and trusted information. Against this background, this paper proposes a novel disaster response system called HAC-ER. Thus HAC-ER interweaves humans and agents, both robotic and software, in social relationships that augment their individual and collective capabilities. To design HAC-ER, we involved end-users including both experts and volunteers in a several participatory design workshops, lab studies, and field trials of increasingly advanced prototypes of individual components of HAC-ER as well as the overall system. This process generated a number of new quantitative and qualitative results but also raised a number of new research questions. HAC-ER thus demonstrates how such Human-Agent Collectives (HACs) can address key challenges in disaster response. Specifically, we show how HAC-ER utilises crowdsourcing combined with machine learning to obtain most important situational awareness from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments, as well as task planning for responders on the ground. Finally, HAC-ER incorporates an infrastructure and the associated intelligence for tracking and utilising the provenance of information shared across the entire system to ensure its accountability. We individually validate each of these elements of HAC-ER and show how they perform against standard (non-HAC) baselines and also elaborate on the evaluation of the overall system.
2026
Tuyen, Nguyen Tan Viet; Georgara, Athina; Singh, Lokesh; Deshmukh, Jayati; Davey, Sean; Tisdale, Paul N.; Ramchurn, Sarvapali
Supporting ?Stop and Watch? in elderly care with socially assistive robots: insights from a participatory design workshop Proceedings Article
In: Baillie, Lynne; Smart, William D.; Graaf, Maartje De; Gombolay, Matthew; Torre, Ilaria (Ed.): Companion Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction, HRI Companion 2026, pp. 302–306, ACM Press, 2026.
Abstract | Links | BibTeX | Tags: assistive living technologies, elderly care, featured_publication, socially assistive robots
@inproceedings{soton511593,
title = {Supporting ?Stop and Watch? in elderly care with socially assistive robots: insights from a participatory design workshop},
author = {Nguyen Tan Viet Tuyen and Athina Georgara and Lokesh Singh and Jayati Deshmukh and Sean Davey and Paul N. Tisdale and Sarvapali Ramchurn},
editor = {Lynne Baillie and William D. Smart and Maartje De Graaf and Matthew Gombolay and Ilaria Torre},
url = {https://eprints.soton.ac.uk/511593/},
year = {2026},
date = {2026-03-01},
urldate = {2026-03-01},
booktitle = {Companion Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction, HRI Companion 2026},
pages = {302–306},
publisher = {ACM Press},
abstract = {ensuremath<pensuremath>?Stop and Watch? is an early-warning tool adopted by the UK NHS and is widely used in elderly care settings. The tool helps caregivers to recognise abnormal changes in residents? health. Despite its clinical value, the process remains highly manual, workload-intensive, and vulnerable to missed observations, particularly in environments facing staff shortages, frequent staff rotations, and increasing care demands. We argue that AI-based systems such as Socially Assistive Robots (SARs) and Assistive Living Technologies (ALTs) offer promising avenues for supporting and enhancing the ?Stop and Watch? tool. However, designing such systems requires a multidisciplinary effort to establish a comprehensive understanding of current practices and the priorities and concerns of all relevant stakeholders. This paper presents insights from a participatory design workshop held in a care home in the UK to explore how SARs and ALTs could meaningfully support the ?Stop and Watch? tool, understand stakeholders? expectations, perceived benefits, and concerns regarding deployment in this sensitive context.ensuremath</pensuremath>},
keywords = {assistive living technologies, elderly care, featured_publication, socially assistive robots},
pubstate = {published},
tppubtype = {inproceedings}
}
ensuremath<pensuremath>?Stop and Watch? is an early-warning tool adopted by the UK NHS and is widely used in elderly care settings. The tool helps caregivers to recognise abnormal changes in residents? health. Despite its clinical value, the process remains highly manual, workload-intensive, and vulnerable to missed observations, particularly in environments facing staff shortages, frequent staff rotations, and increasing care demands. We argue that AI-based systems such as Socially Assistive Robots (SARs) and Assistive Living Technologies (ALTs) offer promising avenues for supporting and enhancing the ?Stop and Watch? tool. However, designing such systems requires a multidisciplinary effort to establish a comprehensive understanding of current practices and the priorities and concerns of all relevant stakeholders. This paper presents insights from a participatory design workshop held in a care home in the UK to explore how SARs and ALTs could meaningfully support the ?Stop and Watch? tool, understand stakeholders? expectations, perceived benefits, and concerns regarding deployment in this sensitive context.ensuremath</pensuremath>
2025
Ramchurn, Gopal; Owen, Aled Lloyd; Ao, Shuang; Barnard, Pepita; Bergin, Aislinn Gomez; Parisio, Isabela; Valoor, Adarsh; Waheed, Maria; Holter, Carolyn Ten; Portillo, Virginia; Procter, Rob; Batterham, Paul; Downer, John; Winter, Peter; Krook, Joshua; Blockx, Jan; Braunschweig, Bertrand; Quintero, Karla; Poretschkin, Maximilian; Becker, Daniel; Campbell-Ratcliffe, Emily; Hawes, Ben; Shaw, Patricia; Patel, Reema; Tewari, Ashish; Thomas, Alec; Duncan, Paul; Gillings, Eliot
Frameworks and Toolkits for Assuring Responsible AI Book Section
In: Responsible Ai UK, University of Southampton, 2025.
Abstract | Links | BibTeX | Tags: featured_publication
@incollection{soton506057,
title = {Frameworks and Toolkits for Assuring Responsible AI},
author = {Gopal Ramchurn and Aled Lloyd Owen and Shuang Ao and Pepita Barnard and Aislinn Gomez Bergin and Isabela Parisio and Adarsh Valoor and Maria Waheed and Carolyn Ten Holter and Virginia Portillo and Rob Procter and Paul Batterham and John Downer and Peter Winter and Joshua Krook and Jan Blockx and Bertrand Braunschweig and Karla Quintero and Maximilian Poretschkin and Daniel Becker and Emily Campbell-Ratcliffe and Ben Hawes and Patricia Shaw and Reema Patel and Ashish Tewari and Alec Thomas and Paul Duncan and Eliot Gillings},
url = {https://eprints.soton.ac.uk/506057/},
year = {2025},
date = {2025-10-01},
urldate = {2025-10-01},
booktitle = {Responsible Ai UK},
publisher = {University of Southampton},
abstract = {This report explores experiences of using tools for assuring responsible practice in the build, deployment, and governance of AI systems, in particular frameworks and toolkits developed and published by RAi UK-funded projects and by other organisations. We examine evidence of how and why these tools are used, and of their usefulness and their limitations. We look for lessons that could improve AI assurance in the future, including potentially using AI-based tools to support AI assurance. The aim is to identify priority areas and key questions for RAi UK and other researchers and organisations to explore further, with the goal of improving the fit between the supply of, and demand for, tools that support organisational assurance of responsible AI .Responsible Ai UK and our partner for this event, Confiance.ai, brought people together from across government, public services, and industry, as well as project teams that develop support for responsible AI. The aim was to clarify how people should approach the development and use of toolkits and/or frameworks in practice. We wanted to synthesise findings from our research across the programme and to find and address any gaps. The workshop took place online on Thursday 10 April 2025.},
keywords = {featured_publication},
pubstate = {published},
tppubtype = {incollection}
}
This report explores experiences of using tools for assuring responsible practice in the build, deployment, and governance of AI systems, in particular frameworks and toolkits developed and published by RAi UK-funded projects and by other organisations. We examine evidence of how and why these tools are used, and of their usefulness and their limitations. We look for lessons that could improve AI assurance in the future, including potentially using AI-based tools to support AI assurance. The aim is to identify priority areas and key questions for RAi UK and other researchers and organisations to explore further, with the goal of improving the fit between the supply of, and demand for, tools that support organisational assurance of responsible AI .Responsible Ai UK and our partner for this event, Confiance.ai, brought people together from across government, public services, and industry, as well as project teams that develop support for responsible AI. The aim was to clarify how people should approach the development and use of toolkits and/or frameworks in practice. We wanted to synthesise findings from our research across the programme and to find and address any gaps. The workshop took place online on Thursday 10 April 2025.
2023
Krook, Joshua; McAuley, Derek; Anderson, Stuart; Downer, John; Winter, Peter; Ramchurn, Sarvapali D
AI Foundation Models: initial review, CMA Consultation, TAS Hub Response Miscellaneous
2023.
Links | BibTeX | Tags: Artificial Intelligence, Competition policy, featured_publication, Foundation Models, Large Language Models, markets
@misc{soton477553,
title = {AI Foundation Models: initial review, CMA Consultation, TAS Hub Response},
author = {Joshua Krook and Derek McAuley and Stuart Anderson and John Downer and Peter Winter and Sarvapali D Ramchurn},
url = {https://eprints.soton.ac.uk/477553/},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
publisher = {University of Southampton},
keywords = {Artificial Intelligence, Competition policy, featured_publication, Foundation Models, Large Language Models, markets},
pubstate = {published},
tppubtype = {misc}
}
Abioye, Ayodeji; Naiseh, Mohammad; Hunt, William; Clark, Jediah R; Ramchurn, Sarvapali D; Soorati, Mohammad
The effect of data visualisation quality and task density on human-swarm interaction Proceedings Article
In: Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), IEEE, 2023.
Abstract | Links | BibTeX | Tags: featured_publication
@inproceedings{soton479970,
title = {The effect of data visualisation quality and task density on human-swarm interaction},
author = {Ayodeji Abioye and Mohammad Naiseh and William Hunt and Jediah R Clark and Sarvapali D Ramchurn and Mohammad Soorati},
url = {https://eprints.soton.ac.uk/479970/},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
booktitle = {Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
publisher = {IEEE},
abstract = {Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.},
keywords = {featured_publication},
pubstate = {published},
tppubtype = {inproceedings}
}
Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.
Worrawichaipat, Phuriwat; Gerding, Enrico; Kaparias, Ioannis; Ramchurn, Sarvapali
Multi-agent signal-less intersection management with dynamic platoon formation Proceedings Article
In: 22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23), pp. 1542–1550, 2023.
Links | BibTeX | Tags: featured_publication
@inproceedings{soton478647,
title = {Multi-agent signal-less intersection management with dynamic platoon formation},
author = {Phuriwat Worrawichaipat and Enrico Gerding and Ioannis Kaparias and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/478647/},
year = {2023},
date = {2023-05-01},
urldate = {2023-05-01},
booktitle = {22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23)},
pages = {1542–1550},
keywords = {featured_publication},
pubstate = {published},
tppubtype = {inproceedings}
}
2016
Ramchurn, Sarvapali; Simpson, Edwin; Fischer, Joel; Huynh, Trung Dong; Ikuno, Yuki; Reece, Steven; Jiang, Wenchao; Wu, Feng; Flann, Jack; Roberts, S. J.; Moreau, Luc; Rodden, T.; Jennings, N. R.
A Disaster Response System based on Human-Agent Collectives Journal Article
In: Journal of Artificial Intelligence Research, vol. 57, pp. 661-708, 2016.
Abstract | Links | BibTeX | Tags: featured_publication
@article{eps374070b,
title = {A Disaster Response System based on Human-Agent Collectives},
author = {Sarvapali Ramchurn and Edwin Simpson and Joel Fischer and Trung Dong Huynh and Yuki Ikuno and Steven Reece and Wenchao Jiang and Feng Wu and Jack Flann and S. J. Roberts and Luc Moreau and T. Rodden and N. R. Jennings},
url = {http://www.jair.org/media/5098/live-5098-9699-jair.pdf},
year = {2016},
date = {2016-12-01},
urldate = {2016-12-01},
journal = {Journal of Artificial Intelligence Research},
volume = {57},
pages = {661-708},
abstract = {Major natural or man-made disasters such as Hurricane Katrina or the 9/11 terror attacks pose significant challenges for emergency responders. First, they have to develop an understanding of the unfolding event either using their own resources or through third-parties such as the local population and agencies. Second, based on the information gathered, they need to deploy their teams in a flexible manner, ensuring that each team performs tasks in The most effective way. Third, given the dynamic nature of a disaster space, and the uncertainties involved in performing rescue missions, information about the disaster space and the actors within it needs to be managed to ensure that responders are always acting on up-to-date and trusted information. Against this background, this paper proposes a novel disaster response system called HAC-ER. Thus HAC-ER interweaves humans and agents, both robotic and software, in social relationships that augment their individual and collective capabilities. To design HAC-ER, we involved end-users including both experts and volunteers in a several participatory design workshops, lab studies, and field trials of increasingly advanced prototypes of individual components of HAC-ER as well as the overall system. This process generated a number of new quantitative and qualitative results but also raised a number of new research questions. HAC-ER thus demonstrates how such Human-Agent Collectives (HACs) can address key challenges in disaster response. Specifically, we show how HAC-ER utilises crowdsourcing combined with machine learning to obtain most important situational awareness from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments, as well as task planning for responders on the ground. Finally, HAC-ER incorporates an infrastructure and the associated intelligence for tracking and utilising the provenance of information shared across the entire system to ensure its accountability. We individually validate each of these elements of HAC-ER and show how they perform against standard (non-HAC) baselines and also elaborate on the evaluation of the overall system.},
keywords = {featured_publication},
pubstate = {published},
tppubtype = {article}
}
Major natural or man-made disasters such as Hurricane Katrina or the 9/11 terror attacks pose significant challenges for emergency responders. First, they have to develop an understanding of the unfolding event either using their own resources or through third-parties such as the local population and agencies. Second, based on the information gathered, they need to deploy their teams in a flexible manner, ensuring that each team performs tasks in The most effective way. Third, given the dynamic nature of a disaster space, and the uncertainties involved in performing rescue missions, information about the disaster space and the actors within it needs to be managed to ensure that responders are always acting on up-to-date and trusted information. Against this background, this paper proposes a novel disaster response system called HAC-ER. Thus HAC-ER interweaves humans and agents, both robotic and software, in social relationships that augment their individual and collective capabilities. To design HAC-ER, we involved end-users including both experts and volunteers in a several participatory design workshops, lab studies, and field trials of increasingly advanced prototypes of individual components of HAC-ER as well as the overall system. This process generated a number of new quantitative and qualitative results but also raised a number of new research questions. HAC-ER thus demonstrates how such Human-Agent Collectives (HACs) can address key challenges in disaster response. Specifically, we show how HAC-ER utilises crowdsourcing combined with machine learning to obtain most important situational awareness from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments, as well as task planning for responders on the ground. Finally, HAC-ER incorporates an infrastructure and the associated intelligence for tracking and utilising the provenance of information shared across the entire system to ensure its accountability. We individually validate each of these elements of HAC-ER and show how they perform against standard (non-HAC) baselines and also elaborate on the evaluation of the overall system.
1.
Tuyen, Nguyen Tan Viet; Georgara, Athina; Singh, Lokesh; Deshmukh, Jayati; Davey, Sean; Tisdale, Paul N.; Ramchurn, Sarvapali
Supporting ?Stop and Watch? in elderly care with socially assistive robots: insights from a participatory design workshop Proceedings Article
In: Baillie, Lynne; Smart, William D.; Graaf, Maartje De; Gombolay, Matthew; Torre, Ilaria (Ed.): Companion Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction, HRI Companion 2026, pp. 302–306, ACM Press, 2026.
@inproceedings{soton511593,
title = {Supporting ?Stop and Watch? in elderly care with socially assistive robots: insights from a participatory design workshop},
author = {Nguyen Tan Viet Tuyen and Athina Georgara and Lokesh Singh and Jayati Deshmukh and Sean Davey and Paul N. Tisdale and Sarvapali Ramchurn},
editor = {Lynne Baillie and William D. Smart and Maartje De Graaf and Matthew Gombolay and Ilaria Torre},
url = {https://eprints.soton.ac.uk/511593/},
year = {2026},
date = {2026-03-01},
urldate = {2026-03-01},
booktitle = {Companion Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction, HRI Companion 2026},
pages = {302–306},
publisher = {ACM Press},
abstract = {ensuremath<pensuremath>?Stop and Watch? is an early-warning tool adopted by the UK NHS and is widely used in elderly care settings. The tool helps caregivers to recognise abnormal changes in residents? health. Despite its clinical value, the process remains highly manual, workload-intensive, and vulnerable to missed observations, particularly in environments facing staff shortages, frequent staff rotations, and increasing care demands. We argue that AI-based systems such as Socially Assistive Robots (SARs) and Assistive Living Technologies (ALTs) offer promising avenues for supporting and enhancing the ?Stop and Watch? tool. However, designing such systems requires a multidisciplinary effort to establish a comprehensive understanding of current practices and the priorities and concerns of all relevant stakeholders. This paper presents insights from a participatory design workshop held in a care home in the UK to explore how SARs and ALTs could meaningfully support the ?Stop and Watch? tool, understand stakeholders? expectations, perceived benefits, and concerns regarding deployment in this sensitive context.ensuremath</pensuremath>},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
ensuremath<pensuremath>?Stop and Watch? is an early-warning tool adopted by the UK NHS and is widely used in elderly care settings. The tool helps caregivers to recognise abnormal changes in residents? health. Despite its clinical value, the process remains highly manual, workload-intensive, and vulnerable to missed observations, particularly in environments facing staff shortages, frequent staff rotations, and increasing care demands. We argue that AI-based systems such as Socially Assistive Robots (SARs) and Assistive Living Technologies (ALTs) offer promising avenues for supporting and enhancing the ?Stop and Watch? tool. However, designing such systems requires a multidisciplinary effort to establish a comprehensive understanding of current practices and the priorities and concerns of all relevant stakeholders. This paper presents insights from a participatory design workshop held in a care home in the UK to explore how SARs and ALTs could meaningfully support the ?Stop and Watch? tool, understand stakeholders? expectations, perceived benefits, and concerns regarding deployment in this sensitive context.ensuremath</pensuremath>
2.
Ramchurn, Gopal; Owen, Aled Lloyd; Ao, Shuang; Barnard, Pepita; Bergin, Aislinn Gomez; Parisio, Isabela; Valoor, Adarsh; Waheed, Maria; Holter, Carolyn Ten; Portillo, Virginia; Procter, Rob; Batterham, Paul; Downer, John; Winter, Peter; Krook, Joshua; Blockx, Jan; Braunschweig, Bertrand; Quintero, Karla; Poretschkin, Maximilian; Becker, Daniel; Campbell-Ratcliffe, Emily; Hawes, Ben; Shaw, Patricia; Patel, Reema; Tewari, Ashish; Thomas, Alec; Duncan, Paul; Gillings, Eliot
Frameworks and Toolkits for Assuring Responsible AI Book Section
In: Responsible Ai UK, University of Southampton, 2025.
@incollection{soton506057,
title = {Frameworks and Toolkits for Assuring Responsible AI},
author = {Gopal Ramchurn and Aled Lloyd Owen and Shuang Ao and Pepita Barnard and Aislinn Gomez Bergin and Isabela Parisio and Adarsh Valoor and Maria Waheed and Carolyn Ten Holter and Virginia Portillo and Rob Procter and Paul Batterham and John Downer and Peter Winter and Joshua Krook and Jan Blockx and Bertrand Braunschweig and Karla Quintero and Maximilian Poretschkin and Daniel Becker and Emily Campbell-Ratcliffe and Ben Hawes and Patricia Shaw and Reema Patel and Ashish Tewari and Alec Thomas and Paul Duncan and Eliot Gillings},
url = {https://eprints.soton.ac.uk/506057/},
year = {2025},
date = {2025-10-01},
urldate = {2025-10-01},
booktitle = {Responsible Ai UK},
publisher = {University of Southampton},
abstract = {This report explores experiences of using tools for assuring responsible practice in the build, deployment, and governance of AI systems, in particular frameworks and toolkits developed and published by RAi UK-funded projects and by other organisations. We examine evidence of how and why these tools are used, and of their usefulness and their limitations. We look for lessons that could improve AI assurance in the future, including potentially using AI-based tools to support AI assurance. The aim is to identify priority areas and key questions for RAi UK and other researchers and organisations to explore further, with the goal of improving the fit between the supply of, and demand for, tools that support organisational assurance of responsible AI .Responsible Ai UK and our partner for this event, Confiance.ai, brought people together from across government, public services, and industry, as well as project teams that develop support for responsible AI. The aim was to clarify how people should approach the development and use of toolkits and/or frameworks in practice. We wanted to synthesise findings from our research across the programme and to find and address any gaps. The workshop took place online on Thursday 10 April 2025.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
This report explores experiences of using tools for assuring responsible practice in the build, deployment, and governance of AI systems, in particular frameworks and toolkits developed and published by RAi UK-funded projects and by other organisations. We examine evidence of how and why these tools are used, and of their usefulness and their limitations. We look for lessons that could improve AI assurance in the future, including potentially using AI-based tools to support AI assurance. The aim is to identify priority areas and key questions for RAi UK and other researchers and organisations to explore further, with the goal of improving the fit between the supply of, and demand for, tools that support organisational assurance of responsible AI .Responsible Ai UK and our partner for this event, Confiance.ai, brought people together from across government, public services, and industry, as well as project teams that develop support for responsible AI. The aim was to clarify how people should approach the development and use of toolkits and/or frameworks in practice. We wanted to synthesise findings from our research across the programme and to find and address any gaps. The workshop took place online on Thursday 10 April 2025.
3.
Krook, Joshua; McAuley, Derek; Anderson, Stuart; Downer, John; Winter, Peter; Ramchurn, Sarvapali D
AI Foundation Models: initial review, CMA Consultation, TAS Hub Response Miscellaneous
2023.
@misc{soton477553,
title = {AI Foundation Models: initial review, CMA Consultation, TAS Hub Response},
author = {Joshua Krook and Derek McAuley and Stuart Anderson and John Downer and Peter Winter and Sarvapali D Ramchurn},
url = {https://eprints.soton.ac.uk/477553/},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
publisher = {University of Southampton},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
4.
Abioye, Ayodeji; Naiseh, Mohammad; Hunt, William; Clark, Jediah R; Ramchurn, Sarvapali D; Soorati, Mohammad
The effect of data visualisation quality and task density on human-swarm interaction Proceedings Article
In: Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), IEEE, 2023.
@inproceedings{soton479970,
title = {The effect of data visualisation quality and task density on human-swarm interaction},
author = {Ayodeji Abioye and Mohammad Naiseh and William Hunt and Jediah R Clark and Sarvapali D Ramchurn and Mohammad Soorati},
url = {https://eprints.soton.ac.uk/479970/},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
booktitle = {Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
publisher = {IEEE},
abstract = {Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.
5.
Worrawichaipat, Phuriwat; Gerding, Enrico; Kaparias, Ioannis; Ramchurn, Sarvapali
Multi-agent signal-less intersection management with dynamic platoon formation Proceedings Article
In: 22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23), pp. 1542–1550, 2023.
@inproceedings{soton478647,
title = {Multi-agent signal-less intersection management with dynamic platoon formation},
author = {Phuriwat Worrawichaipat and Enrico Gerding and Ioannis Kaparias and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/478647/},
year = {2023},
date = {2023-05-01},
urldate = {2023-05-01},
booktitle = {22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23)},
pages = {1542–1550},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
6.
Ramchurn, Sarvapali; Simpson, Edwin; Fischer, Joel; Huynh, Trung Dong; Ikuno, Yuki; Reece, Steven; Jiang, Wenchao; Wu, Feng; Flann, Jack; Roberts, S. J.; Moreau, Luc; Rodden, T.; Jennings, N. R.
A Disaster Response System based on Human-Agent Collectives Journal Article
In: Journal of Artificial Intelligence Research, vol. 57, pp. 661-708, 2016.
@article{eps374070b,
title = {A Disaster Response System based on Human-Agent Collectives},
author = {Sarvapali Ramchurn and Edwin Simpson and Joel Fischer and Trung Dong Huynh and Yuki Ikuno and Steven Reece and Wenchao Jiang and Feng Wu and Jack Flann and S. J. Roberts and Luc Moreau and T. Rodden and N. R. Jennings},
url = {http://www.jair.org/media/5098/live-5098-9699-jair.pdf},
year = {2016},
date = {2016-12-01},
urldate = {2016-12-01},
journal = {Journal of Artificial Intelligence Research},
volume = {57},
pages = {661-708},
abstract = {Major natural or man-made disasters such as Hurricane Katrina or the 9/11 terror attacks pose significant challenges for emergency responders. First, they have to develop an understanding of the unfolding event either using their own resources or through third-parties such as the local population and agencies. Second, based on the information gathered, they need to deploy their teams in a flexible manner, ensuring that each team performs tasks in The most effective way. Third, given the dynamic nature of a disaster space, and the uncertainties involved in performing rescue missions, information about the disaster space and the actors within it needs to be managed to ensure that responders are always acting on up-to-date and trusted information. Against this background, this paper proposes a novel disaster response system called HAC-ER. Thus HAC-ER interweaves humans and agents, both robotic and software, in social relationships that augment their individual and collective capabilities. To design HAC-ER, we involved end-users including both experts and volunteers in a several participatory design workshops, lab studies, and field trials of increasingly advanced prototypes of individual components of HAC-ER as well as the overall system. This process generated a number of new quantitative and qualitative results but also raised a number of new research questions. HAC-ER thus demonstrates how such Human-Agent Collectives (HACs) can address key challenges in disaster response. Specifically, we show how HAC-ER utilises crowdsourcing combined with machine learning to obtain most important situational awareness from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments, as well as task planning for responders on the ground. Finally, HAC-ER incorporates an infrastructure and the associated intelligence for tracking and utilising the provenance of information shared across the entire system to ensure its accountability. We individually validate each of these elements of HAC-ER and show how they perform against standard (non-HAC) baselines and also elaborate on the evaluation of the overall system.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Major natural or man-made disasters such as Hurricane Katrina or the 9/11 terror attacks pose significant challenges for emergency responders. First, they have to develop an understanding of the unfolding event either using their own resources or through third-parties such as the local population and agencies. Second, based on the information gathered, they need to deploy their teams in a flexible manner, ensuring that each team performs tasks in The most effective way. Third, given the dynamic nature of a disaster space, and the uncertainties involved in performing rescue missions, information about the disaster space and the actors within it needs to be managed to ensure that responders are always acting on up-to-date and trusted information. Against this background, this paper proposes a novel disaster response system called HAC-ER. Thus HAC-ER interweaves humans and agents, both robotic and software, in social relationships that augment their individual and collective capabilities. To design HAC-ER, we involved end-users including both experts and volunteers in a several participatory design workshops, lab studies, and field trials of increasingly advanced prototypes of individual components of HAC-ER as well as the overall system. This process generated a number of new quantitative and qualitative results but also raised a number of new research questions. HAC-ER thus demonstrates how such Human-Agent Collectives (HACs) can address key challenges in disaster response. Specifically, we show how HAC-ER utilises crowdsourcing combined with machine learning to obtain most important situational awareness from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments, as well as task planning for responders on the ground. Finally, HAC-ER incorporates an infrastructure and the associated intelligence for tracking and utilising the provenance of information shared across the entire system to ensure its accountability. We individually validate each of these elements of HAC-ER and show how they perform against standard (non-HAC) baselines and also elaborate on the evaluation of the overall system.
Selected Publications
Multi-agent signal-less intersection management with dynamic platoon formation
No data available.
https://eprints.soton.ac.uk/478647/
@inproceedings{soton478647,
title = {Multi-agent signal-less intersection management with dynamic platoon formation},
author = {Phuriwat Worrawichaipat and Enrico Gerding and Ioannis Kaparias and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/478647/},
year = {2023},
date = {2023-05-01},
booktitle = {22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23)},
pages = {1542--1550},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
title = {Multi-agent signal-less intersection management with dynamic platoon formation},
author = {Phuriwat Worrawichaipat and Enrico Gerding and Ioannis Kaparias and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/478647/},
year = {2023},
date = {2023-05-01},
booktitle = {22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23)},
pages = {1542--1550},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
AI Foundation Models: initial review, CMA Consultation, TAS Hub Response
No data available.
https://eprints.soton.ac.uk/477553/
@misc{soton477553,
title = {AI Foundation Models: initial review, CMA Consultation, TAS Hub Response},
author = {Joshua Krook and Derek McAuley and Stuart Anderson and John Downer and Peter Winter and Sarvapali D Ramchurn},
url = {https://eprints.soton.ac.uk/477553/},
year = {2023},
date = {2023-06-01},
publisher = {University of Southampton},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
title = {AI Foundation Models: initial review, CMA Consultation, TAS Hub Response},
author = {Joshua Krook and Derek McAuley and Stuart Anderson and John Downer and Peter Winter and Sarvapali D Ramchurn},
url = {https://eprints.soton.ac.uk/477553/},
year = {2023},
date = {2023-06-01},
publisher = {University of Southampton},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
The effect of data visualisation quality and task density on human-swarm interaction
No data available.
https://eprints.soton.ac.uk/479970/
@inproceedings{soton479970,
title = {The effect of data visualisation quality and task density on human-swarm interaction},
author = {Ayodeji Abioye and Mohammad Naiseh and William Hunt and Jediah R Clark and Sarvapali D Ramchurn and Mohammad Soorati},
url = {https://eprints.soton.ac.uk/479970/},
year = {2023},
date = {2023-06-01},
booktitle = {Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
publisher = {IEEE},
abstract = {Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
title = {The effect of data visualisation quality and task density on human-swarm interaction},
author = {Ayodeji Abioye and Mohammad Naiseh and William Hunt and Jediah R Clark and Sarvapali D Ramchurn and Mohammad Soorati},
url = {https://eprints.soton.ac.uk/479970/},
year = {2023},
date = {2023-06-01},
booktitle = {Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
publisher = {IEEE},
abstract = {Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Demonstrating performance benefits of human-swarm teaming
Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.
https://eprints.soton.ac.uk/479903/
@inproceedings{soton479903,
title = {Demonstrating performance benefits of human-swarm teaming},
author = {William Hunt and Jack Ryan and Ayodeji O Abioye and Sarvapali D Ramchurn and Mohammad D Soorati},
url = {https://eprints.soton.ac.uk/479903/},
year = {2023},
date = {2023-05-01},
booktitle = {Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
pages = {3062--3064},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)},
abstract = {Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
title = {Demonstrating performance benefits of human-swarm teaming},
author = {William Hunt and Jack Ryan and Ayodeji O Abioye and Sarvapali D Ramchurn and Mohammad D Soorati},
url = {https://eprints.soton.ac.uk/479903/},
year = {2023},
date = {2023-05-01},
booktitle = {Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
pages = {3062--3064},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)},
abstract = {Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2026
Tuyen, Nguyen Tan Viet; Georgara, Athina; Singh, Lokesh; Deshmukh, Jayati; Davey, Sean; Tisdale, Paul N.; Ramchurn, Sarvapali
Supporting ?Stop and Watch? in elderly care with socially assistive robots: insights from a participatory design workshop Proceedings Article
In: Baillie, Lynne; Smart, William D.; Graaf, Maartje De; Gombolay, Matthew; Torre, Ilaria (Ed.): Companion Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction, HRI Companion 2026, pp. 302–306, ACM Press, 2026.
@inproceedings{soton511593,
title = {Supporting ?Stop and Watch? in elderly care with socially assistive robots: insights from a participatory design workshop},
author = {Nguyen Tan Viet Tuyen and Athina Georgara and Lokesh Singh and Jayati Deshmukh and Sean Davey and Paul N. Tisdale and Sarvapali Ramchurn},
editor = {Lynne Baillie and William D. Smart and Maartje De Graaf and Matthew Gombolay and Ilaria Torre},
url = {https://eprints.soton.ac.uk/511593/},
year = {2026},
date = {2026-03-01},
urldate = {2026-03-01},
booktitle = {Companion Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction, HRI Companion 2026},
pages = {302–306},
publisher = {ACM Press},
abstract = {ensuremath<pensuremath>?Stop and Watch? is an early-warning tool adopted by the UK NHS and is widely used in elderly care settings. The tool helps caregivers to recognise abnormal changes in residents? health. Despite its clinical value, the process remains highly manual, workload-intensive, and vulnerable to missed observations, particularly in environments facing staff shortages, frequent staff rotations, and increasing care demands. We argue that AI-based systems such as Socially Assistive Robots (SARs) and Assistive Living Technologies (ALTs) offer promising avenues for supporting and enhancing the ?Stop and Watch? tool. However, designing such systems requires a multidisciplinary effort to establish a comprehensive understanding of current practices and the priorities and concerns of all relevant stakeholders. This paper presents insights from a participatory design workshop held in a care home in the UK to explore how SARs and ALTs could meaningfully support the ?Stop and Watch? tool, understand stakeholders? expectations, perceived benefits, and concerns regarding deployment in this sensitive context.ensuremath</pensuremath>},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
ensuremath<pensuremath>?Stop and Watch? is an early-warning tool adopted by the UK NHS and is widely used in elderly care settings. The tool helps caregivers to recognise abnormal changes in residents? health. Despite its clinical value, the process remains highly manual, workload-intensive, and vulnerable to missed observations, particularly in environments facing staff shortages, frequent staff rotations, and increasing care demands. We argue that AI-based systems such as Socially Assistive Robots (SARs) and Assistive Living Technologies (ALTs) offer promising avenues for supporting and enhancing the ?Stop and Watch? tool. However, designing such systems requires a multidisciplinary effort to establish a comprehensive understanding of current practices and the priorities and concerns of all relevant stakeholders. This paper presents insights from a participatory design workshop held in a care home in the UK to explore how SARs and ALTs could meaningfully support the ?Stop and Watch? tool, understand stakeholders? expectations, perceived benefits, and concerns regarding deployment in this sensitive context.ensuremath</pensuremath>
2025
Ramchurn, Gopal; Owen, Aled Lloyd; Ao, Shuang; Barnard, Pepita; Bergin, Aislinn Gomez; Parisio, Isabela; Valoor, Adarsh; Waheed, Maria; Holter, Carolyn Ten; Portillo, Virginia; Procter, Rob; Batterham, Paul; Downer, John; Winter, Peter; Krook, Joshua; Blockx, Jan; Braunschweig, Bertrand; Quintero, Karla; Poretschkin, Maximilian; Becker, Daniel; Campbell-Ratcliffe, Emily; Hawes, Ben; Shaw, Patricia; Patel, Reema; Tewari, Ashish; Thomas, Alec; Duncan, Paul; Gillings, Eliot
Frameworks and Toolkits for Assuring Responsible AI Book Section
In: Responsible Ai UK, University of Southampton, 2025.
@incollection{soton506057,
title = {Frameworks and Toolkits for Assuring Responsible AI},
author = {Gopal Ramchurn and Aled Lloyd Owen and Shuang Ao and Pepita Barnard and Aislinn Gomez Bergin and Isabela Parisio and Adarsh Valoor and Maria Waheed and Carolyn Ten Holter and Virginia Portillo and Rob Procter and Paul Batterham and John Downer and Peter Winter and Joshua Krook and Jan Blockx and Bertrand Braunschweig and Karla Quintero and Maximilian Poretschkin and Daniel Becker and Emily Campbell-Ratcliffe and Ben Hawes and Patricia Shaw and Reema Patel and Ashish Tewari and Alec Thomas and Paul Duncan and Eliot Gillings},
url = {https://eprints.soton.ac.uk/506057/},
year = {2025},
date = {2025-10-01},
urldate = {2025-10-01},
booktitle = {Responsible Ai UK},
publisher = {University of Southampton},
abstract = {This report explores experiences of using tools for assuring responsible practice in the build, deployment, and governance of AI systems, in particular frameworks and toolkits developed and published by RAi UK-funded projects and by other organisations. We examine evidence of how and why these tools are used, and of their usefulness and their limitations. We look for lessons that could improve AI assurance in the future, including potentially using AI-based tools to support AI assurance. The aim is to identify priority areas and key questions for RAi UK and other researchers and organisations to explore further, with the goal of improving the fit between the supply of, and demand for, tools that support organisational assurance of responsible AI .Responsible Ai UK and our partner for this event, Confiance.ai, brought people together from across government, public services, and industry, as well as project teams that develop support for responsible AI. The aim was to clarify how people should approach the development and use of toolkits and/or frameworks in practice. We wanted to synthesise findings from our research across the programme and to find and address any gaps. The workshop took place online on Thursday 10 April 2025.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
This report explores experiences of using tools for assuring responsible practice in the build, deployment, and governance of AI systems, in particular frameworks and toolkits developed and published by RAi UK-funded projects and by other organisations. We examine evidence of how and why these tools are used, and of their usefulness and their limitations. We look for lessons that could improve AI assurance in the future, including potentially using AI-based tools to support AI assurance. The aim is to identify priority areas and key questions for RAi UK and other researchers and organisations to explore further, with the goal of improving the fit between the supply of, and demand for, tools that support organisational assurance of responsible AI .Responsible Ai UK and our partner for this event, Confiance.ai, brought people together from across government, public services, and industry, as well as project teams that develop support for responsible AI. The aim was to clarify how people should approach the development and use of toolkits and/or frameworks in practice. We wanted to synthesise findings from our research across the programme and to find and address any gaps. The workshop took place online on Thursday 10 April 2025.
2023
Krook, Joshua; McAuley, Derek; Anderson, Stuart; Downer, John; Winter, Peter; Ramchurn, Sarvapali D
AI Foundation Models: initial review, CMA Consultation, TAS Hub Response Miscellaneous
2023.
@misc{soton477553,
title = {AI Foundation Models: initial review, CMA Consultation, TAS Hub Response},
author = {Joshua Krook and Derek McAuley and Stuart Anderson and John Downer and Peter Winter and Sarvapali D Ramchurn},
url = {https://eprints.soton.ac.uk/477553/},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
publisher = {University of Southampton},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Abioye, Ayodeji; Naiseh, Mohammad; Hunt, William; Clark, Jediah R; Ramchurn, Sarvapali D; Soorati, Mohammad
The effect of data visualisation quality and task density on human-swarm interaction Proceedings Article
In: Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), IEEE, 2023.
@inproceedings{soton479970,
title = {The effect of data visualisation quality and task density on human-swarm interaction},
author = {Ayodeji Abioye and Mohammad Naiseh and William Hunt and Jediah R Clark and Sarvapali D Ramchurn and Mohammad Soorati},
url = {https://eprints.soton.ac.uk/479970/},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
booktitle = {Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
publisher = {IEEE},
abstract = {Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.
Worrawichaipat, Phuriwat; Gerding, Enrico; Kaparias, Ioannis; Ramchurn, Sarvapali
Multi-agent signal-less intersection management with dynamic platoon formation Proceedings Article
In: 22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23), pp. 1542–1550, 2023.
@inproceedings{soton478647,
title = {Multi-agent signal-less intersection management with dynamic platoon formation},
author = {Phuriwat Worrawichaipat and Enrico Gerding and Ioannis Kaparias and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/478647/},
year = {2023},
date = {2023-05-01},
urldate = {2023-05-01},
booktitle = {22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23)},
pages = {1542–1550},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2016
Ramchurn, Sarvapali; Simpson, Edwin; Fischer, Joel; Huynh, Trung Dong; Ikuno, Yuki; Reece, Steven; Jiang, Wenchao; Wu, Feng; Flann, Jack; Roberts, S. J.; Moreau, Luc; Rodden, T.; Jennings, N. R.
A Disaster Response System based on Human-Agent Collectives Journal Article
In: Journal of Artificial Intelligence Research, vol. 57, pp. 661-708, 2016.
@article{eps374070b,
title = {A Disaster Response System based on Human-Agent Collectives},
author = {Sarvapali Ramchurn and Edwin Simpson and Joel Fischer and Trung Dong Huynh and Yuki Ikuno and Steven Reece and Wenchao Jiang and Feng Wu and Jack Flann and S. J. Roberts and Luc Moreau and T. Rodden and N. R. Jennings},
url = {http://www.jair.org/media/5098/live-5098-9699-jair.pdf},
year = {2016},
date = {2016-12-01},
urldate = {2016-12-01},
journal = {Journal of Artificial Intelligence Research},
volume = {57},
pages = {661-708},
abstract = {Major natural or man-made disasters such as Hurricane Katrina or the 9/11 terror attacks pose significant challenges for emergency responders. First, they have to develop an understanding of the unfolding event either using their own resources or through third-parties such as the local population and agencies. Second, based on the information gathered, they need to deploy their teams in a flexible manner, ensuring that each team performs tasks in The most effective way. Third, given the dynamic nature of a disaster space, and the uncertainties involved in performing rescue missions, information about the disaster space and the actors within it needs to be managed to ensure that responders are always acting on up-to-date and trusted information. Against this background, this paper proposes a novel disaster response system called HAC-ER. Thus HAC-ER interweaves humans and agents, both robotic and software, in social relationships that augment their individual and collective capabilities. To design HAC-ER, we involved end-users including both experts and volunteers in a several participatory design workshops, lab studies, and field trials of increasingly advanced prototypes of individual components of HAC-ER as well as the overall system. This process generated a number of new quantitative and qualitative results but also raised a number of new research questions. HAC-ER thus demonstrates how such Human-Agent Collectives (HACs) can address key challenges in disaster response. Specifically, we show how HAC-ER utilises crowdsourcing combined with machine learning to obtain most important situational awareness from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments, as well as task planning for responders on the ground. Finally, HAC-ER incorporates an infrastructure and the associated intelligence for tracking and utilising the provenance of information shared across the entire system to ensure its accountability. We individually validate each of these elements of HAC-ER and show how they perform against standard (non-HAC) baselines and also elaborate on the evaluation of the overall system.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Major natural or man-made disasters such as Hurricane Katrina or the 9/11 terror attacks pose significant challenges for emergency responders. First, they have to develop an understanding of the unfolding event either using their own resources or through third-parties such as the local population and agencies. Second, based on the information gathered, they need to deploy their teams in a flexible manner, ensuring that each team performs tasks in The most effective way. Third, given the dynamic nature of a disaster space, and the uncertainties involved in performing rescue missions, information about the disaster space and the actors within it needs to be managed to ensure that responders are always acting on up-to-date and trusted information. Against this background, this paper proposes a novel disaster response system called HAC-ER. Thus HAC-ER interweaves humans and agents, both robotic and software, in social relationships that augment their individual and collective capabilities. To design HAC-ER, we involved end-users including both experts and volunteers in a several participatory design workshops, lab studies, and field trials of increasingly advanced prototypes of individual components of HAC-ER as well as the overall system. This process generated a number of new quantitative and qualitative results but also raised a number of new research questions. HAC-ER thus demonstrates how such Human-Agent Collectives (HACs) can address key challenges in disaster response. Specifically, we show how HAC-ER utilises crowdsourcing combined with machine learning to obtain most important situational awareness from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments, as well as task planning for responders on the ground. Finally, HAC-ER incorporates an infrastructure and the associated intelligence for tracking and utilising the provenance of information shared across the entire system to ensure its accountability. We individually validate each of these elements of HAC-ER and show how they perform against standard (non-HAC) baselines and also elaborate on the evaluation of the overall system.