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- W4377231387 abstract "Given a user who asks why an algorithmic decision did not satisfy some conditions, a counterfactual explanation takes the form of a minimally perturbed input that would have led to a decision satisfying the user’s conditions. Building on recent work, this paper develops techniques to generate counterfactual explanations for linear discrete constrained optimization problems. These explanations take the form of a minimally perturbed objective vector that induces an optimal solution satisfying the newly stated user constraints. Drawing inspiration from the inverse combinatorial optimization literature, we introduce a novel non-convex quadratic programming algorithm to generate such explanations. Furthermore, we develop conditions for the existence of an explanation, addressing a limitation of past approaches. Finally, we discuss several future directions for explanations in discrete optimization such as actionable and sparse explanations." @default.
- W4377231387 created "2023-05-23" @default.
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- W4377231387 date "2023-01-01" @default.
- W4377231387 modified "2023-09-29" @default.
- W4377231387 title "Objective-Based Counterfactual Explanations for Linear Discrete Optimization" @default.
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- W4377231387 doi "https://doi.org/10.1007/978-3-031-33271-5_2" @default.
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