UKRI CDT in Accountable, Responsible and Transparent AI

Specialists with perspectives: ART-AI’s interdisciplinary professional experts make the best, and safest, use of artificial intelligence (AI) and explore the opportunities, challenges and constraints presented by the diverse range of contexts for AI.

The University of Bath Campus

ART-AI offers a uniquely interdisciplinary doctoral training approach, educating students from a range of backgrounds across computer science and artificial intelligence, engineering and technology, and humanities and social sciences.

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.

 

News

PhD Success for ART-AI students!

We are thrilled to announce that several ART-AI students have recently earned their PhDs!

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New ART-AI seminar videos

If you have missed any of the latest ART-AI seminars you can catch up with the recordings here.

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ART-AI students graduate!

Congratulations to our recent ART-AI graduates.

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Events

ART-AI Writing Retreat 2026

Our students are looking forward to once again spending 3 days at a residential Writing Retreat at the Ammerdown Retreat Centre this August.

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International Conference on Scientific Computing and Machine Learning 2026

The International Conference on Scientific Computing and Machine Learning 2026 will take place at the University of Bath on the 14th-17th September 2026.

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From Tacit to Explicit: The Art and Science of Accountable Human-Machine Teaming with Kris Lee

We are pleased to welcome Kris Lee, Founder and CEO of ArtSci AI, for this joint ART-AI and AI/ML Group seminar, entitled “From Tacit to Explicit: The Art and Science of Accountable Human-Machine Teaming”, on Tuesday 27 October 2026, from 10:00am to 11:00am (GMT).

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