Explainable AI Seminars @ Imperial

CLArg Group, Centre for eXplainable Artificial Intelligence, Imperial College London

XAI has witnessed unprecedented growth in both academia and industry in recent years (alongside AI itself), given its crucial role in supporting human-AI partnerships. XAI allows to deploy opaque data-driven AI methods safely in a variety of settings, such as finance, healthcare and law. XAI is positioned at the intersection of AI, human-computer interaction, the social sciences (in particular psychology) and applications.
Overall, XAI is increasingly part of all AI policies on ethics, trustworthiness and safety of AI. This seminar series focuses on all aspects of XAI, ranging from methods to applications.

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Next Seminar

Paul Kobialka - Counterfactual Strategies for Markov Decision Processes

18^th June, 15:00 BST, on Microsoft Teams (join online).

Abstract

Counterfactuals are widely used in AI to explain how minimal changes to a model’s input can lead to a different output. However, established methods for computing counterfactuals typically focus on one-step decision-making, and are not directly applicable to sequential decision-making tasks. This paper fills this gap by introducing counterfactual strategies for Markov Decision Processes (MDPs). During MDP execution, a strategy decides which of the enabled actions (with known probabilistic effects) to execute next. Given an initial strategy that reaches an undesired outcome with a probability above some limit, we identify minimal changes to the initial strategy to reduce that probability below the limit. We encode such counterfactual strategies as solutions to non-linear optimization problems, and further extend our encoding to synthesize diverse counterfactual strategies. We evaluate our approach on four real-world datasets and demonstrate its practical viability in sophisticated sequential decision-making tasks.

Bio

Paul Kobialka finished his Master degree in Computer Science at RWTH Aachen University in 2021 and is since then a Ph.D. fellow at the University of Oslo. He is researching data-driven formal methods, i.e. problems where formal guarantees are necessary but formal models are too complex to construct traditionally. In his current work, he applies formal methods to improve user experiences in digital services.

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