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As machine learning black boxes are increasingly being deployed in criticaldomains such as healthcare and criminal justice, there has been a growingemphasis on developing techniques for explaining these black boxes in a humaninterpretable manner. It has recently become apparent that a high-fidelityexplanation of a black box ML model may not accurately reflect the biases inthe black box. As a consequence, explanations have the potential to misleadhuman users into trusting a problematic black box. In this work, we rigorouslyexplore the notion of misleading explanations and how they influence user trustin black-box models. More specifically, we propose a novel theoreticalframework for understanding and generating misleading explanations, and carryout a user study with domain experts to demonstrate how these explanations canbe used to mislead users. Our work is the first to empirically establish howuser trust in black box models can be manipulated via misleading explanations.

"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations