Metadata
Title
Oxford Robotics Institute
Category
undergraduate
UUID
153839e771fa4a0784f5ae2d60b019d4
Source URL
https://ori.ox.ac.uk/projects/rails
Parent URL
https://ori.ox.ac.uk/projects
Crawl Time
2026-03-09T03:24:41+00:00
Rendered Raw Markdown

Oxford Robotics Institute

Source: https://ori.ox.ac.uk/projects/rails Parent: https://ori.ox.ac.uk/projects

Responsible AI for Long-term Trustworthy Autonomous Systems (RAILS)

Our starting point in RAILS is the understanding that autonomous systems (AS) do not exist in stasis - they are frequently designed for dynamic environments, and may also be designed to change themselves over time. RAILS tackles the challenges associated with the long-term operation of autonomous systems and the effects of change on these systems. In particular, we will focus on two main challenges that have hitherto been little-studied: 

(i) open-ended dynamic environments and 

(ii) lifelong learning systems 

RAILS will explore independent long-term autonomy systems in different applications. These will include i) autonomous vehicles and ii) autonomous robot systems such as unmanned aerial vehicles (drones). These categories of system have been chosen because although operating in different environments, they share enough common features that some aspects of the work will apply to all use-cases, while other areas will be unique. Moreover, RAILS builds out from the TAS RoAD project  which investigates responsibility in the context of autonomous vehicle (AV) data. RAILS will extend this work from data to processes (e.g. risk assessment and learning) and from autonomous vehicles to autonomous robot systems (e.g. drones).

RAILS draws on interdisciplinary research expertise from engineering, law, social science, and computer science to interrogate regulations, standards, methodologies, impacts, and public acceptance around the long-term operation of autonomous systems from technical, ethical, legal and social perspectives. RAILS is founded in a responsible innovation approach that seeks to foreground prospective conceptions of responsibility. The technical and contextual questions cannot be isolated from each other - RAILS will therefore address both autonomous systems and the legal and social frameworks within which they operate. The common factor across these socio-legal frameworks is their connection to dimensions of responsibility, care and accountability. These responsibility-dimensions are directly related to public perceptions of trustworthiness, so RAILS’ work is rooted in these perceptions in order to offer a trans-disciplinary framework that incorporates nuanced, context-specific and varied understandings of responsibility.

Driverless Futures (ERC)

RoAD - Responsible AV Data

SAX - Sense-Assess-eXplain

RAILS Project Team

[#### Lars Kunze

Lead Investigator, Oxford Robotics Institute - University of Oxford](#)

[#### Marina Jirotka

Co-Investigator, Department of Computer Science - University of Oxford](#)

[#### Richard Hawkins

Co-Investigator, Department of Computer Science - University of York](#)

[#### Jo-Ann Pattinson

Co-Investigator, Institute for Transport Studies - University of Leeds](#)

[#### Jack Stilgoe

Co-Investigator, Department of Science & Technology Studies - University College London](#)

[#### Carolyn Ten Holter

Researcher, Department of Computer Science - University of Oxford](#)

[#### Pericle Salvini

Advisor to the project, Department of Computer Science - University of Oxford](#)

[#### Jonathan Attias

Systems Engineer, Oxford Robotics Institute, University of Oxford](#)

[#### Acacia Nockolds

Project Manager, Oxford Robotics Institute - University of Oxford](#)

[#### Sarah Baldwin

Project Manager, Department of Computer Science - University of Oxford](#)