Causal-Manipulationist Approaches to Explaining Machine Learning

Abstract

Explanatory Artificial Intelligence (XAI) is receiving considerable attention from philosophers of science. A prevalent strategy is to integrate machine learning (ML) algorithms into existing accounts of scientific explanation. This chapter delves into causal approaches, specifically examining two recent interpretations of XAI: “the explanation game” by Watson and Floridi, and Buijsman's causal counterfactual approach. Both aim to integrate ML into Woodward's causal-manipulationist framework. Both have the goal of answering how an algorithm generates a given output. While making significant progress within the debate on XAI, both approaches fall short in key considerations about scientific explanation, particularly in addressing why-seeking questions. This chapter explores the conditions under which said questions can be answered. Two standard issues in the literature on philosophy of scientific explanation are addressed: identifying explanatory relations of dependence and (an alternative to) the factivity condition.