Designing an Interpretable Interface for Contextual Bandits
<|reference_start|>Designing an Interpretable Interface for Contextual Bandits: 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.<|reference_end|>
@article{maher2024designing,
    title={Designing an Interpretable Interface for Contextual Bandits},
    author={Andrew Maher, Matia Gobbo, Lancelot Lachartre, Subash Prabanantham,
  Rowan Swiers and Puli Liyanagama},
    journal={arXiv preprint arXiv:2409.15143},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15143},
    primaryClass={cs.LG stat.ML}
}