Explainable AI under contract and tort law: legal incentives and technical challenges
By Philipp Hacker, Ralf Krestel, Stefan Grundmann, Felix Naumann. 2020
125 citations recorded by the source corpus.
This paper shows that the law, in subtle ways, may set hitherto unrecognized incentives for the adoption of explainable machine learning applications. In doing so, we make two novel contributions. First, on the legal side, we show that to avoid liability, professional actors, such as doctors and managers, may soon be legally compelled to use explainable ML models. We argue that the importance of explainability reaches far beyond data protection law, and crucially influences questions of contractual and tort liability for the use of ML models. To this effect, we conduct two legal case studies, in medical and corporate merger applications of ML. As a second contribution, we discuss the (legally required) trade-off between accuracy and explainability and demonstrate the effect in a technical case study in the context of spam classification.
Subfields: Artificial Intelligence, Health Informatics, Safety Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Aram Galstyan
- Fred Morstatter
- Ninareh Mehrabi
- Greg Ver Steeg
- Umang Gupta
- Effy Vayena
- Fei Ye
- Adrian G. Bors
In Safety Research
Citations in the corpus
- Attributing Fair Decisions with Attention Interventions (2021, background): “As machine learning continues to be adopted into fields where discriminatory treatments can lead to legal penalties, fairness and interpretability have become a necessity and a legal incentive in addition to an ethical responsibility (Barocas and Selbst 2016; Hacker et al. 2020).”
- Explainability for artificial intelligence in healthcare: a multidisciplinary perspective (2020, methodology): “Hacker et al. argue that legally it is likely that explainability will be a prerequisite from a contract and tort law perspective where doctors may have to use a certain tool to avoid the threat of a medical malpractice lawsuit [17].”
- Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research (2022, unknown): “[169] NB and RF 2020 √ √ Self explainable [171] RNN and CNN 2021 √ √ √ √ √ LIME”