X Hacking: The Threat of Misguided AutoML

• URL: http://arxiv.org/abs/2401.08513v2
• Date: Mon, 12 Feb 2024 14:53:33 GMT
• Title: X Hacking: The Threat of Misguided AutoML
• Authors: Rahul Sharma, Sergey Redyuk, Sumantrak Mukherjee, Andrea Sipka, Sebastian Vollmer, David Selby
• Abstract summary: This paper introduces the concept of X-hacking, a form of p-hacking applied to XAI metrics such as Shap values. We show how an automated machine learning pipeline can be used to search for 'defensible' models that produce a desired explanation while maintaining superior performance to a common baseline.
• Score: 2.3011205420794574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Explainable AI (XAI) and interpretable machine learning methods help to build trust in model predictions and derived insights, yet also present a perverse incentive for analysts to manipulate XAI metrics to support pre-specified conclusions. This paper introduces the concept of X-hacking, a form of p-hacking applied to XAI metrics such as Shap values. We show how an automated machine learning pipeline can be used to search for 'defensible' models that produce a desired explanation while maintaining superior predictive performance to a common baseline. We formulate the trade-off between explanation and accuracy as a multi-objective optimization problem and illustrate the feasibility and severity of X-hacking empirically on familiar real-world datasets. Finally, we suggest possible methods for detection and prevention, and discuss ethical implications for the credibility and reproducibility of XAI research.

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