AlgoIntent

PhD Candidate, Computer Science, UCL, UK

Research Interests

Ashton, H. (2020) Definitions of intent for AI derived from common law, 14th International Workshop on Juris-informatics (JURISIN 2020)
Ashton, H (2021) Campbell-Goodhart’s law and Reinforcement Learning, ICAART 2021
Ashton, H (2020) AI Legal Counsel to train and regulate legally constrained Autonomous systems, 4th annual workshop of applications of artificial intelligence in the legal industry, IEEE BigData 2020
Ashton H (2021) Definitions of intent suitable for algorithms (pre-print)
Ashton H (2021) Extending counterfactual accounts of intent to include oblique intent (pre-print)
Ashton, H (2022) Defining and Identifying the legal culpability of side effects using causal graphs, AAAI workshop on Artificial Intelligence Safety (SafeAI22)
Ashton, H and Franklin, M (2022) The problem of behaviour and preference manipulation in AI systems, AAAI workshop on Artificial Intelligence Safety (SafeAI22)
Ashton, H, Franklin, M and Lagnado, D (Forthcoming) Testing a definition of intent in a legal setting (preprint)
Franklin, M; Ashton, H; Gorman, R and Armstrong, S (2022) Missing mechanisms of manipulation in the EU AI Act, 35^th International Conference of Florida Artificial Intelligence Research Society (FLAIRS)
Franklin, M; Ashton, H; Awad, E and Lagnado, D (2022) Causal Framework of AI Responsibility, Fifth AAAI/ACM Conference on Artificial Intelligence, Ethics and Society