Bias-Aware Design for Informed Decisions: Raising Awareness of Self-Selection Bias in User Ratings and Reviews

Abstract: People often take user ratings and reviews into consideration when shopping for products or services online. However, such user-generated data contains self-selection bias that could affect people decisions and it is hard to resolve this issue completely by algorithms. In this work, we propose to raise the awareness of the self-selection bias by making three types of information concerning user ratings and reviews transparent. We distill these three pieces of information (reviewers experience, the extremity of emotion, and reported aspects) from the definition of self-selection bias and exploration of related literature. We further conduct an online survey to assess the perceptions of the usefulness of such information and identify the exact facets people care about in their decision process. Then, we propose a visual design to make such details behind user reviews transparent and integrate the design into an experimental website for evaluation. The results of a between-subjects study demonstrate that our bias-aware design significantly increases the awareness of bias and their satisfaction with decision-making. We further offer a series of design implications for improving information transparency and awareness of bias in user-generated content.