Ashton, H., & Franklin, M. (2022). A Method to Check that Participants Really are Imagining Artificial Minds When Ascribing Mental States. null (pp. 470-474). doi:10.1007/978-3-031-19682-9_59

Ashton, H. (2022). Defining and Identifying the Legal Culpability of Side Effects Using Causal Graphs. Proceedings of the Workshop on Artificial Intelligence Safety 2022 (SafeAI 2022), 3087. CEUR Workshop Proceedings.

Ashton, H. (2022). Definitions of intent suitable for algorithms. Artificial Intelligence and Law. doi:10.1007/s10506-022-09322-x

Ashton, H. (2022). Law breaking trading algorithms: Emergence and deterrence (Doctoral dissertation). UCL (University College London).

Franklin, M., Ashton, H., Gorman, R., & Armstrong, S. (2022). Missing Mechanisms of Manipulation in the EU AI Act. Proceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS, 35. University of Florida George A Smathers Libraries. doi:10.32473/flairs.v35i.130723

Ashton, H., Franklin, M., Gorman, R., & Armstrong, S. (2022). Recognising the importance of preference change: A call for a coordinated multidisciplinary research effort in the age of AI.

Ashton, H., & Franklin, M. (2022). The problem of behaviour and preference manipulation in AI systems. Proceedings of the Workshop on Artificial Intelligence Safety 2022 (SafeAI 2022), 3087. CEUR Workshop Proceedings.

Ashton, H. (2021). Causal Campbell-Goodhart's law and reinforcement learning. ICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence, 2, 67-73.

Ashton, H. (2021). Extending counterfactual accounts of intent to include oblique intent. .

Ashton, H. (2021). What criminal and civil law tells us about Safe RL techniques to generate law-abiding behaviour. Proceedings of the Workshop on Artificial Intelligence Safety 2021 (SafeAI 2021), 2808. CEUR Workshop Proceedings.