Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness

13 Jan 2020Michael HindDennis WeiYunfeng Zhang

Many proposed methods for explaining machine learning predictions are in fact challenging to understand for nontechnical consumers. This paper builds upon an alternative consumer-driven approach called TED that asks for explanations to be provided in training data, along with target labels... (read more)

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