Conceptual Challenges for Interpretable Machine Learning

40 Pages Posted: 20 Aug 2020 Last revised: 27 Aug 2021

Date Written: August 6, 2020

Abstract

As machine learning has gradually entered into ever more sectors of public and private life, there has been a growing demand for algorithmic explainability. How can we make the predictions of complex statistical models more intelligible to end users? A sub-discipline of computer science known as interpretable machine learning (iML) has emerged to address this urgent question. Numerous influential methods have been proposed, from local linear approximations to rule lists and counterfactuals. In this article, I highlight three conceptual challenges that are largely overlooked by authors in this area. I argue that the vast majority of iML algorithms are plagued by:

(1) ambiguity with respect to their true target;

(2) a disregard for error rates and severe testing; and

(3) an emphasis on product over process.

Each point is developed at length, drawing on relevant debates in epistemology and philosophy of science. Examples and counterexamples from iML are considered, demonstrating how failure to acknowledge these problems can result in counter-intuitive and potentially misleading explanations. Without greater care for the conceptual foundations of iML, future work in this area is doomed to repeat the same mistakes.

Keywords: Artificial Intelligence, Algorithmic Explainability, Interpret-Able Machine Learning, Scientific Explanation, Severe Testing

Suggested Citation

Watson, David, Conceptual Challenges for Interpretable Machine Learning (August 6, 2020). Available at SSRN: https://ssrn.com/abstract=3668444 or http://dx.doi.org/10.2139/ssrn.3668444

David Watson (Contact Author)

King's College London ( email )

Strand
London, England WC2R 2LS
United Kingdom

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
181
Abstract Views
980
Rank
300,534
PlumX Metrics