Related papers: Data Science Principles for Interpretable and Explainable AI

Data Science Principles for Interpretable and Explainable AI

URL: http://arxiv.org/abs/2405.10552v2
Date: Sat, 17 Aug 2024 22:08:38 GMT
Title: Data Science Principles for Interpretable and Explainable AI
Authors: Kris Sankaran,
Abstract summary: Interpretable and interactive machine learning aims to make complex models more transparent and controllable. This review synthesizes key principles from the growing literature in this field.
Score: 0.7581664835990121
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: Society's capacity for algorithmic problem-solving has never been greater. Artificial Intelligence is now applied across more domains than ever, a consequence of powerful abstractions, abundant data, and accessible software. As capabilities have expanded, so have risks, with models often deployed without fully understanding their potential impacts. Interpretable and interactive machine learning aims to make complex models more transparent and controllable, enhancing user agency. This review synthesizes key principles from the growing literature in this field. We first introduce precise vocabulary for discussing interpretability, like the distinction between glass box and explainable models. We then explore connections to classical statistical and design principles, like parsimony and the gulfs of interaction. Basic explainability techniques -- including learned embeddings, integrated gradients, and concept bottlenecks -- are illustrated with a simple case study. We also review criteria for objectively evaluating interpretability approaches. Throughout, we underscore the importance of considering audience goals when designing interactive data-driven systems. Finally, we outline open challenges and discuss the potential role of data science in addressing them. Code to reproduce all examples can be found at https://go.wisc.edu/3k1ewe.

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