Reducing prejudice with counter-stereotypical AI

Abstract

Based on a review of relevant literature, we propose that the proliferation of AI with human-like and social features presents an unprecedented opportunity to address the underlying cognitive and affective drivers of prejudice. An approach informed by the psychology of intergroup contact and prejudice reduction is necessary because current AI systems often reinforce or avoid prejudices. Against this backdrop, we outline unique opportunities for prejudice reduction through ‘synthetic’ intergroup contact, wherein consumers interact with AI products and services that counter stereotypes and serve as a ‘proxy’ members of the outgroup (i.e., counter-stereotypical AI). In contrast to human-human contact, humanizing and socializing AI can reduce prejudice through more repeated, direct, unavoidable, private, non-judgmental, collaborative, and need-satisfying contact. We illustrate the potential of synthetic intergroup contact with counter-stereotypical AI using examples of gender stereotypes and hate speech and discuss practical considerations for implementing counter-stereotypical AI without inadvertently perpetuating or reinforcing prejudice.