"How do I fool you?": Manipulating User Trust via Misleading Black Box
  Explanations
  As machine learning black boxes are increasingly being deployed in critical
domains such as healthcare and criminal justice, there has been a growing
emphasis on developing techniques for explaining these black boxes in a human
interpretable manner. It has recently become apparent that a high-fidelity
explanation of a black box ML model may not accurately reflect the biases in
the black box. As a consequence, explanations have the potential to mislead
human users into trusting a problematic black box. In this work, we rigorously
explore the notion of misleading explanations and how they influence user trust
in black-box models. More specifically, we propose a novel theoretical
framework for understanding and generating misleading explanations, and carry
out a user study with domain experts to demonstrate how these explanations can
be used to mislead users. Our work is the first to empirically establish how
user trust in black box models can be manipulated via misleading explanations.
