TS-Insight: Visualizing Thompson Sampling for Verification and XAI
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Abstract (arXiv)
Thompson Sampling (TS) and its variants are powerful Multi-Armed Bandit algorithms used to balance exploration and exploitation strategies in active learning. Yet, their probabilistic nature often turns them into a "black box", hindering debugging and trust. We introduce TS-Insight, a visual analytics tool explicitly designed to shed light on the internal decision mechanisms of Thompson Sampling-based algorithms, for model developers. It comprises multiple plots, tracing for each arm the evolving posteriors, evidence counts, and sampling outcomes, enabling the verification, diagnosis, and explainability of exploration/exploitation dynamics. This tool aims at fostering trust and facilitating effective debugging and deployment in complex binary decision-making scenarios especially in sensitive domains requiring interpretable decision-making.
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References · 5
- 2017A Tutorial on Thompson Samplingcited by 433
- 2015Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challengescited by 114
- 2017Taming Non-stationary Bandits: A Bayesian Approachcited by 32
- 2022The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizationscited by 29
- 2023Algorithm Selection for Deep Active Learning with Imbalanced Datasetscited by 18
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