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Measuring the Impact of Explanation Bias: A Study of Natural Language Justifications for Recommender Systems

Andrey Petrov
Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (CHI EA '23), ACM


Despite the potential impact of explanations on decision making, there is a lack of research on quantifying the effect of explanations on users' choices. This paper presents an experimental protocol for measuring the degree to which positively or negatively biased explanations can lead to users choosing suboptimal recommendations. Key elements of this protocol include a preference elicitation stage to allow for personalizing recommendations, manual identification and extraction of item aspects from reviews, and a controlled method for introducing bias through the combination of both positive and negative aspects. We also present explanations in two different textual formats: as a list of item aspects and as fluent natural language text. Through a user study with 129 participants, we demonstrate that explanations can significantly affect users' selections and that these findings generalize across explanation formats.