Building Human-Machine Trust via Interpretability

Developing human-machine trust is a prerequisite for adoption of machine learning systems in decision critical settings (e. g healthcare and governance). Users develop appropriate trust in these systems when they understand how the systems make their decisions. Interpretability not only helps users understand what a system learns but also helps users contest that system to align with their intuition. We propose an algorithm, AVA: Aggregate Valuation of Antecedents, that generates a consensus feature attribution, retrieving local explanations and capturing global patterns learned by a model. Our empirical results show that AVA rivals current benchmarks.

Authors

• Umang Bhatt New York University The Alan Turing Institute
• Pradeep Ravikumar
• Jos´e M. F. Moura

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