Counterfactual Instances Explain Little

TL;DR

This paper argues that counterfactual instances alone are insufficient for explanations in AI, emphasizing the need for causal equations to provide meaningful understanding of machine learning decisions.

Contribution

It introduces the idea that effective explanations require both counterfactual instances and causal equations, challenging current methods that rely solely on counterfactuals.

Findings

Counterfactual instances alone explain little about ML decisions.

Combining causal equations with counterfactuals improves explanations.

Proposes a framework integrating causal models with counterfactual explanations.

Abstract

In many applications, it is important to be able to explain the decisions of machine learning systems. An increasingly popular approach has been to seek to provide \emph{counterfactual instance explanations}. These specify close possible worlds in which, contrary to the facts, a person receives their desired decision from the machine learning system. This paper will draw on literature from the philosophy of science to argue that a satisfactory explanation must consist of both counterfactual instances and a causal equation (or system of equations) that support the counterfactual instances. We will show that counterfactual instances by themselves explain little. We will further illustrate how explainable AI methods that provide both causal equations and counterfactual instances can successfully explain machine learning predictions.