English

TS-Insight: Visualizing Thompson Sampling for Verification and XAI

Human-Computer Interaction 2025-08-22 v2 Artificial Intelligence Machine Learning Machine Learning

Abstract

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.

Keywords

Cite

@article{arxiv.2507.19898,
  title  = {TS-Insight: Visualizing Thompson Sampling for Verification and XAI},
  author = {Parsa Vares and Éloi Durant and Jun Pang and Nicolas Médoc and Mohammad Ghoniem},
  journal= {arXiv preprint arXiv:2507.19898},
  year   = {2025}
}

Comments

Accepted as a poster at IEEE VIS 2025 ("TS-Insight: Visual Fingerprinting of Multi-Armed Bandits"). Open-source tool available at https://github.com/LIST-LUXEMBOURG/ts-insight

R2 v1 2026-07-01T04:20:07.180Z