Discrimination in the evaluation of others is a key cause of social inequality around the world. However, relatively little is known about psychological interventions that can be used to prevent biased evaluations. The limited evidence that exists on these strategies is spread across many methods and populations, making it difficult to generate reliable best practices that can be effective across contexts. In the present work, we held a research contest to solicit interventions with the goal of reducing discrimination based on physical attractiveness using a hypothetical admissions task. Thirty interventions were tested across four rounds of data collection (total N > 20,000). Using a Signal Detection Theory approach to evaluate interventions, we identified two interventions that reduced discrimination by lessening both decision noise and decision bias, while two other interventions reduced overall discrimination by only lessening noise or bias. The most effective interventions largely provided concrete strategies that directed participants’ attention towards decision-relevant criteria and away from socially biasing information, though the fact that very similar interventions produced differing effects on discrimination suggests certain key characteristics that are needed for manipulations to reliably impact judgment. The effects of these four interventions on decision bias, noise, or both also replicated in a different discrimination domain, political affiliation, and generalized to populations with self-reported hiring experience. Results of the contest for decreasing attractiveness-based favoritism suggest that identifying effective routes for changing discriminatory behavior is a challenge, and that greater investment is needed to develop impactful, flexible, and scalable strategies for reducing discrimination
