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arXiv · 2409.15143

Designing an Interpretable Interface for Contextual Bandits

Andrew Maher·Matia Gobbo·Lancelot Lachartre·Subash Prabanantham·Rowan Swiers·Puli Liyanagama
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Abstract

Contextual bandits have become an increasingly popular solution for personalized recommender systems. Despite their growing use, the interpretability of these systems remains a significant challenge, particularly for the often non-expert operators tasked with ensuring their optimal performance. In this paper, we address this challenge by designing a new interface to explain to domain experts the underlying behaviour of a bandit. Central is a metric we term "value gain", a measure derived from off-policy evaluation to quantify the real-world impact of sub-components within a bandit. We conduct a qualitative user study to evaluate the effectiveness of our interface. Our findings suggest that by carefully balancing technical rigour with accessible presentation, it is possible to empower non-experts to manage complex machine learning systems. We conclude by outlining guiding principles that other researchers should consider when building similar such interfaces in future.

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Andrew Maher, Matia Gobbo, Lancelot Lachartre, Subash Prabanantham, Rowan Swiers, Puli Liyanagama. 2024-09-23. Designing an Interpretable Interface for Contextual Bandits. https://arxiv.org/abs/2409.15143

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