# Liability regimes in the age of AI: a use-case driven analysis of the burden of proof

arXiv: https://arxiv.org/abs/2211.01817 · HTML: https://mlsanity.com/paper/2211.01817/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 65 references that are in the map.
- First posted on arXiv 3 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications](https://mlsanity.com/paper/2103.04244/) (2021)
- [Socially Responsible AI Algorithms: Issues, Purposes, and Challenges](https://mlsanity.com/paper/2101.02032/) (2021)
- [Taking Principles Seriously: A Hybrid Approach to Value Alignment](https://mlsanity.com/paper/2012.11705/) (2020)
- [Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges](https://mlsanity.com/paper/1901.04730/) (2019)
- [The AI Liability Puzzle and A Fund-Based Work-Around](https://mlsanity.com/paper/1911.08005/) (2019)

## Built on by (most cited first, 4 of 4)

- [Predictable Artificial Intelligence](https://mlsanity.com/paper/2310.06167/) (2023)
- [Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness](https://mlsanity.com/paper/2403.14641/) (2024)
- [Generating Causal Explanations of Vehicular Agent Behavioural Interactions with Learnt Reward Profiles](https://mlsanity.com/paper/2503.14557/) (2025)
- [Responsible Personalisation: The Double-Edged Sword of Personalisation in Human-Robot Interaction](https://mlsanity.com/paper/2607.06344/) (2026)

## Abstract (arXiv)

New emerging technologies powered by Artificial Intelligence (AI) have the potential to disruptively transform our societies for the better. In particular, data-driven learning approaches (i.e., Machine Learning (ML)) have been a true revolution in the advancement of multiple technologies in various application domains. But at the same time there is growing concern about certain intrinsic characteristics of these methodologies that carry potential risks to both safety and fundamental rights. Although there are mechanisms in the adoption process to minimize these risks (e.g., safety regulations), these do not exclude the possibility of harm occurring, and if this happens, victims should be able to seek compensation. Liability regimes will therefore play a key role in ensuring basic protection for victims using or interacting with these systems. However, the same characteristics that make AI systems inherently risky, such as lack of causality, opacity, unpredictability or their self and continuous learning capabilities, may lead to considerable difficulties when it comes to proving causation. This paper presents three case studies, as well as the methodology to reach them, that illustrate these difficulties. Specifically, we address the cases of cleaning robots, delivery drones and robots in education. The outcome of the proposed analysis suggests the need to revise liability regimes to alleviate the burden of proof on victims in cases involving AI technologies.