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Disagreement amongst counterfactual explanations: how transparency can be misleading

  • Dieter Brughmans [1] ; Lissa Melis [2] ; David Martens [1]
    1. [1] University of Antwerp

      University of Antwerp

      Arrondissement Antwerpen, Bélgica

    2. [2] Civil and Environmental Engineering Department, Pennsylvania State University, 212 Sackett Building, University Park, PA, 16802, USA School of Business and Economics, Maastricht University, Tongersestraat 53, Maastricht, 6211 LM, The Netherlands
  • Localización: Top, ISSN-e 1863-8279, ISSN 1134-5764, Vol. 32, Nº. Extra 3, 2024 (Ejemplar dedicado a: Mathematical Optimization and Machine Learning), págs. 429-462
  • Idioma: inglés
  • DOI: 10.1007/s11750-024-00670-2
  • Enlaces
  • Resumen

    • Counterfactual explanations are increasingly used as an Explainable Artifcial Intelligence (XAI) technique to provide stakeholders of complex machine learning algorithms with explanations for data-driven decisions. The popularity of counterfactual explanations resulted in a boom in the algorithms generating them. However, not every algorithm creates uniform explanations for the same instance. Even though in some contexts multiple possible explanations are benefcial, there are circumstances where diversity amongst counterfactual explanations results in a potential disagreement problem among stakeholders. Ethical issues arise when for example, malicious agents use this diversity to fairwash an unfair machine learning model by hiding sensitive features. As legislators worldwide tend to start including the right to explanations for data-driven, high-stakes decisions in their policies, these ethical issues should be understood and addressed. Our literature review on the disagreement problem in XAI reveals that this problem has never been empirically assessed for counterfactual explanations. Therefore, in this work, we conduct a large-scale empirical analysis, on 40 data sets, using 12 explanation-generating methods, for two black-box models, yielding over 192,000 explanations. Our study fnds alarmingly high disagreement levels between the methods tested. A malicious user is able to both exclude and include desired features when multiple counterfactual explanations are available. This disagreement seems to be driven mainly by the data set characteristics and the type of counterfactual algorithm. XAI centers on the transparency of algorithmic decision-making, but our analysis advocates for transparency about this self-proclaimed transparency.

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