Related papers: Consequence-aware Sequential Counterfactual Generation

Consequence-aware Sequential Counterfactual Generation

URL: http://arxiv.org/abs/2104.05592v1
Date: Mon, 12 Apr 2021 16:10:03 GMT
Title: Consequence-aware Sequential Counterfactual Generation
Authors: Philip Naumann and Eirini Ntoutsi
Abstract summary: We propose a model-agnostic method for sequential counterfactual generation. Our approach generates less costly solutions, is more efficient, and provides the user with a diverse set of solutions to choose from.
Score: 5.71097144710995
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Counterfactuals have become a popular technique nowadays for interacting with black-box machine learning models and understanding how to change a particular instance to obtain a desired outcome from the model. However, most existing approaches assume instant materialization of these changes, ignoring that they may require effort and a specific order of application. Recently, methods have been proposed that also consider the order in which actions are applied, leading to the so-called sequential counterfactual generation problem. In this work, we propose a model-agnostic method for sequential counterfactual generation. We formulate the task as a multi-objective optimization problem and present an evolutionary approach to find optimal sequences of actions leading to the counterfactuals. Our cost model considers not only the direct effect of an action, but also its consequences. Experimental results show that compared to state of the art, our approach generates less costly solutions, is more efficient, and provides the user with a diverse set of solutions to choose from.

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