2024
Drury, M. R. F; Miles, O; Brundell, P; Farina, L; Webb, H; Giannachi, G; J Moore,; Benford, S; Jordan, S; Stahl, B; Vallejos, E Perez; Vear, C
2024.
@workshop{nokey,
title = {CReAting a Dynamic archive of responsibLe AI Ecosystems in the context of Creative AI (CRADLE): Workshop Report 2: Cat Royale},
author = {M.R.F Drury and O Miles and P Brundell and L Farina and H Webb and G Giannachi and J Moore, and S Benford and S Jordan and B Stahl and E Perez Vallejos and C Vear},
url = {https://www.nottingham.ac.uk/humanities/documents/philosophy/ai-ecosystems/workshop-report-2-cat-royale.pdf},
year = {2024},
date = {2024-03-25},
urldate = {2024-03-25},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
Gooding, P; Booker, L; Bouich, A; Callaghan, S; Spooner, R; Thorpe, K
iREAL: Indigenising Requirements Elicitation for Artificial Intelligence in Libraries Presentation
27.02.2024.
@misc{nokey,
title = {iREAL: Indigenising Requirements Elicitation for Artificial Intelligence in Libraries},
author = {P Gooding and L Booker and A Bouich and S Callaghan and R Spooner and K Thorpe},
url = {https://eprints.gla.ac.uk/332213/},
year = {2024},
date = {2024-02-27},
urldate = {2024-02-27},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
Miles, Oliver; Webb, Helena; Farina, Lydia; Giannachi, Gabriella; Benford, Steve; Moore, John; Jordan, Spencer; Stahl, Bernd; Perez-Vallejos, Elvira; Vear, Craig
2024.
@workshop{nokey,
title = {CReAting a Dynamic archive of responsibLe Ecosystems in the context of creative AI (CRADLE): Workshop Report 1: JESS+ A Digiscore},
author = {Oliver Miles and Helena Webb and Lydia Farina and Gabriella Giannachi and Steve Benford and John Moore and Spencer Jordan and Bernd Stahl and Elvira Perez-Vallejos and
Craig Vear},
url = {https://www.nottingham.ac.uk/humanities/documents/philosophy/ai-ecosystems/workshop-report-1-jess-a-digiscore.pdf},
year = {2024},
date = {2024-02-14},
urldate = {2024-02-14},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
2023
Brennan, Jenny; Groves, Lara; Jones, Elliot; Strait, Andrew
AI assurance? Assessing and mitigating risks across the AI lifecycle Miscellaneous
2023.
@misc{nokey,
title = {AI assurance? Assessing and mitigating risks across the AI lifecycle},
author = {Jenny Brennan and Lara Groves and Elliot Jones and Andrew Strait},
url = {https://www.adalovelaceinstitute.org/report/risks-ai-systems/},
year = {2023},
date = {2023-07-18},
urldate = {2023-07-18},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Jones, Elliot; Birtwistle, Michael; Reeve, Octavia
Keeping an eye on AI: Approaches to government monitoring of the AI landscape Miscellaneous
2023.
@misc{nokey,
title = {Keeping an eye on AI: Approaches to government monitoring of the AI landscape},
author = {Elliot Jones and Michael Birtwistle and Octavia Reeve},
url = {https://www.adalovelaceinstitute.org/report/keeping-an-eye-on-ai/},
year = {2023},
date = {2023-07-18},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Brown, Ian
Allocating accountability in AI supply chains: a UK-centred regulatory perspective Miscellaneous
2023, ISBN: 978-1-7392615-2-8.
@misc{nokey,
title = {Allocating accountability in AI supply chains: a UK-centred regulatory perspective},
author = {Ian Brown},
url = {https://www.adalovelaceinstitute.org/resource/ai-supply-chains/},
isbn = {978-1-7392615-2-8},
year = {2023},
date = {2023-06-29},
urldate = {2023-06-29},
abstract = {Creating an artificial intelligence (AI) system is a collaborative effort that involves many actors and sources of knowledge. Whether simple or
complex, built in-house or by an external developer, AI systems often rely on complex supply chains, each involving a network of actors responsible for various aspects of the system’s training and development.
As policymakers seek to develop a regulatory framework for AI technologies, it will be crucial for them to understand how these different
supply chains work, and how to assign relevant, distinct responsibilities to the appropriate actor in each supply chain. Policymakers must also
recognise that not all actors in supply chains will be equally resourced, and regulation will need to take account of these realities.
Depending on the supply chain, some companies (perhaps UK small businesses) supplying services directly to customers will not have the
power, access or capability to address or mitigate all risks or harms that may arise.
This paper aims to help policymakers and regulators explore the challenges and nuances of different AI supply chains, and provides a
conceptual framework for how they might apply different responsibilities in the regulation of AI systems. },
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Creating an artificial intelligence (AI) system is a collaborative effort that involves many actors and sources of knowledge. Whether simple or
complex, built in-house or by an external developer, AI systems often rely on complex supply chains, each involving a network of actors responsible for various aspects of the system’s training and development.
As policymakers seek to develop a regulatory framework for AI technologies, it will be crucial for them to understand how these different
supply chains work, and how to assign relevant, distinct responsibilities to the appropriate actor in each supply chain. Policymakers must also
recognise that not all actors in supply chains will be equally resourced, and regulation will need to take account of these realities.
Depending on the supply chain, some companies (perhaps UK small businesses) supplying services directly to customers will not have the
power, access or capability to address or mitigate all risks or harms that may arise.
This paper aims to help policymakers and regulators explore the challenges and nuances of different AI supply chains, and provides a
conceptual framework for how they might apply different responsibilities in the regulation of AI systems.
complex, built in-house or by an external developer, AI systems often rely on complex supply chains, each involving a network of actors responsible for various aspects of the system’s training and development.
As policymakers seek to develop a regulatory framework for AI technologies, it will be crucial for them to understand how these different
supply chains work, and how to assign relevant, distinct responsibilities to the appropriate actor in each supply chain. Policymakers must also
recognise that not all actors in supply chains will be equally resourced, and regulation will need to take account of these realities.
Depending on the supply chain, some companies (perhaps UK small businesses) supplying services directly to customers will not have the
power, access or capability to address or mitigate all risks or harms that may arise.
This paper aims to help policymakers and regulators explore the challenges and nuances of different AI supply chains, and provides a
conceptual framework for how they might apply different responsibilities in the regulation of AI systems.
Jones, Bronwyn; Luger, Ewa; Jones, Rhia
Generative AI & journalism: A rapid risk-based review Miscellaneous
2023.
@misc{Jones2023,
title = {Generative AI & journalism: A rapid risk-based review},
author = {Bronwyn Jones and Ewa Luger and Rhia Jones},
url = {https://www.pure.ed.ac.uk/ws/portalfiles/portal/372212564/GenAI_Journalism_Rapid_Risk_Review_June_2023_BJ_RJ_EL.pdf
https://www.research.ed.ac.uk/en/publications/generative-ai-amp-journalism-a-rapid-risk-based-review},
year = {2023},
date = {2023-06-06},
urldate = {2023-06-06},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
