AI Legal Counsel to train and regulate legally constrained Autonomous systems. - Hal Ashton (IEEE BigData, 2020)

What criminal and civil law tells us about Safe RL techniques to generate law-abiding behaviour. - Hal Ashton (SafeAI@AAAI, 2021)

Causal Campbell-Goodhart's Law and Reinforcement Learning. - Hal Ashton (ICAART (2), 2021)

Extending counterfactual accounts of intent to include oblique intent. - Hal Ashton (CoRR, 2021)

Defining and Identifying the Legal Culpability of Side Effects using Causal Graphs. - Hal Ashton (SafeAI@AAAI, 2022)

The Problem of Behaviour and Preference Manipulation in AI Systems. - Hal Ashton; Matija Franklin (SafeAI@AAAI, 2022)

Causal Framework of Artificial Autonomous Agent Responsibility. - Matija Franklin; Hal Ashton; Edmond Awad; David A. Lagnado (AIES, 2022)

Missing Mechanisms of Manipulation in the EU AI Act. - Matija Franklin; Hal Ashton; Rebecca Gormann; Stuart Armstrong (FLAIRS, 2022)

A Method to Check that Participants Really are Imagining Artificial Minds When Ascribing Mental States. - Hal Ashton; Matija Franklin (HCI (49), 2022)

Recognising the importance of preference change: A call for a coordinated multidisciplinary research effort in the age of AI. - Matija Franklin; Hal Ashton; Rebecca Gormann; Stuart Armstrong (CoRR, 2022)

Preference Change in Persuasive Robotics. - Matija Franklin; Hal Ashton (CoRR, 2022)

Solutions to preference manipulation in recommender systems require knowledge of meta-preferences. - Hal Ashton; Matija Franklin (CoRR, 2022)

Concept Extrapolation: A Conceptual Primer. - Matija Franklin; Rebecca Gormann; Hal Ashton; Stuart Armstrong (CoRR, 2023)

Definitions of intent suitable for algorithms. - Hal Ashton (Artif. Intell. Law, 2023)

Beyond Preferences in AI Alignment. - Tan Zhi-Xuan; Micah Carroll; Matija Franklin; Hal Ashton (CoRR, 2024)

Model-Free RL Agents Demonstrate System 1-Like Intentionality. - Hal Ashton; Matija Franklin (CoRR, 2025)