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Details of Grant 

EPSRC Reference: EP/W011344/1
Title: Responsible AI for Long-term Trustworthy Autonomous Systems (RAILS): Integrating Responsible AI and Socio-legal Governance
Principal Investigator: Kunze, Dr L
Other Investigators:
Jirotka, Professor M Hawkins, Dr R Stilgoe, Professor J
Pattinson, Dr J J
Researcher Co-Investigators:
Dr P P Salvini
Project Partners:
Addleshaw Goddard LLP Amazon Web Services (Not UK) Autonomous Drivers Alliance
Imperium Drive Oxbotica Ltd Society for Computers and Law
TRL Ltd (Transport Research Laboratory)
Department: Engineering Science
Organisation: University of Oxford
Scheme: Standard Research
Starts: 01 January 2022 Ends: 30 June 2024 Value (£): 710,089
EPSRC Research Topic Classifications:
Artificial Intelligence Governance
EPSRC Industrial Sector Classifications:
Transport Systems and Vehicles
Related Grants:
Panel History:
Panel DatePanel NameOutcome
29 Sep 2021 Trustworthy Autonomous Systems Programme - Responsibility Interview Panel Announced
Summary on Grant Application Form
Society is seeing enormous growth in the development and implementation of autonomous systems, which can offer significant benefits to citizens, communities, and businesses. The potential for improvements in societal wellbeing is substantial. However, this positive potential is balanced by a similar potential for societal harm through contingent effects such as the environmental footprint of autonomous systems, systemic disadvantage for some socio-economic groups, and entrenchment of digital divides. The rollout of autonomous systems must therefore be addressed with responsibilities to society in mind. This must include engaging in dialogue with society and with those affected, trying to anticipate challenges before they occur, and responding to them.

One such anticipated challenge is the effect of change on autonomous systems. Autonomous systems are not designed to be deployed in conditions of perfect stasis, as they are unlikely to encounter such conditions in real-world environments. They are frequently designed for changing environments, like public roads, and may also be designed to change themselves over time, for instance by means of learning capabilities. Not only that, but these changes in deployed systems and in their operating conditions are also likely to take place against a shifting contextual background of societal alteration (e.g. other technologies, 'black swan' events, or simply the day-to-day operation of communities). The effects of such change, on the systems themselves, on the environments within which they are operating, and on the humans with which they engage, must be considered as part of a responsible innovation approach.

The RAILS project brings together a team from UCL and the Universities of York, Leeds and Oxford, from multiple disciplines, with the aim of engaging with the challenges associated with the long-term operation of autonomous systems and the effects of change on these systems. In particular, we will explore how the notion of responsibility is affected by

(i) open-ended dynamic environments - situations that change over time, and

(ii) lifelong-learning systems - i.e. systems that are designed to adapt themselves to their circumstances and 'learn' over time.

The RAILS project will focus on such independent long-term autonomous systems in different applications. These will include (i) autonomous vehicles and (ii) autonomous robot systems such as unmanned aerial vehicles (drones). RAILS will look at social and legal contexts, as well as technical requirements, in order to assess whether and how these systems can be designed, developed, and operated in a way that they are responsible, accountable, and trustworthy.

The overall aim of the RAILS project is to bring together responsible development principles with governance mechanisms and technical understanding to create new understandings of how autonomous systems can adapt to change, how they can be deployed in a responsible and trustworthy way, and how such deployment can be framed by governance to ensure accountability and flexibility.

Key Findings
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Organisation Website: http://www.ox.ac.uk