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

Understand

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.