Computer Science > Machine Learning
[Submitted on 27 May 2019 (v1), last revised 28 Feb 2020 (this version, v5)]
Title:Model-Agnostic Counterfactual Explanations for Consequential Decisions
View PDFAbstract:Predictive models are being increasingly used to support consequential decision making at the individual level in contexts such as pretrial bail and loan approval. As a result, there is increasing social and legal pressure to provide explanations that help the affected individuals not only to understand why a prediction was output, but also how to act to obtain a desired outcome. To this end, several works have proposed optimization-based methods to generate nearest counterfactual explanations. However, these methods are often restricted to a particular subset of models (e.g., decision trees or linear models) and differentiable distance functions. In contrast, we build on standard theory and tools from formal verification and propose a novel algorithm that solves a sequence of satisfiability problems, where both the distance function (objective) and predictive model (constraints) are represented as logic formulae. As shown by our experiments on real-world data, our algorithm is: i) model-agnostic ({non-}linear, {non-}differentiable, {non-}convex); ii) data-type-agnostic (heterogeneous features); iii) distance-agnostic ($\ell_0, \ell_1, \ell_\infty$, and combinations thereof); iv) able to generate plausible and diverse counterfactuals for any sample (i.e., 100% coverage); and v) at provably optimal distances.
Submission history
From: Amir-Hossein Karimi [view email][v1] Mon, 27 May 2019 13:22:39 UTC (341 KB)
[v2] Tue, 28 May 2019 08:00:19 UTC (341 KB)
[v3] Tue, 8 Oct 2019 10:21:41 UTC (2,980 KB)
[v4] Fri, 14 Feb 2020 16:49:52 UTC (1,993 KB)
[v5] Fri, 28 Feb 2020 16:24:45 UTC (1,993 KB)
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