About ART-AI

We are a UKRI funded Centre for Doctoral Training (CDT) in Accountable, Responsible and Transparent AI.

students interacting with an artificial intelligence

ART-AI exists to educate interdisciplinary professional experts to make the best, and safest, use of artificial intelligence (AI) and to explore the opportunities, challenges and constraints presented by the diverse range of contexts for AI. We bring together researchers from across the University to train the next generation of specialists with expertise in AI, its applications and its implications.

Now in our second year, over the course of the centre’s life we will recruit and train at least 60 PhD students from diverse backgrounds, including existing experts in AI, but also from engineering, social science and policy backgrounds to help ensure that developments in AI, and decisions on how and when to use it or not, are informed and ethical. ART-AI will produce interdisciplinary graduates who can act as leaders and innovators with the knowledge to make the right decisions on what is possible, what is desirable, and how AI can be ethically, safely and effectively deployed.

ART-AI draws together a wide range of topics: from algorithms to ethics; robotics safety to computational and public policy; probabilistic machine learning to symbolic AI; provenance, transparency and uncertainty quantification to intelligibility and trust in heterogeneous intelligent systems; reinforcement learning to emotion in human-machine interaction; and many others.

Research Themes Include

Transparency and Intelligibility

Making AI systems accessible, accountable, open to inspection and intelligible to diverse stakeholders.

Risk and Decision Making with AI

How to assess its risks and benefits, quantitatively and qualitatively.

Safety and Trust in Human-Machine Systems

How to approach diverse challenges from robotics safety to computational and public policy.

Policy-Making with and for AI

The effects of AI in public policy design as it becomes endemic in a broad range of work environments.

Innovation in Data-Driven & Classical AI

What innovations are needed to provide policy-driven, explainable and auditable support for human work.

Engineering Applications of AI

How to apply AI techniques to solve real world engineering problems .