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Many proposed methods for explaining machine learning predictions are in factchallenging to understand for nontechnical consumers. This paper builds upon analternative consumer-driven approach called TED that asks for explanations tobe provided in training data, along with target labels. Using semi-syntheticdata from credit approval and employee retention applications, experiments areconducted to investigate some practical considerations with TED, including itsperformance with different classification algorithms, varying numbers ofexplanations, and variability in explanations. A new algorithm is proposed tohandle the case where some training examples do not have explanations. Ourresults show that TED is robust to increasing numbers of explanations, noisyexplanations, and large fractions of missing explanations, thus making advancestoward its practical deployment.

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