How Machine-Generated Ratings and Social Exposure Affect Human Reviewers: Evidence from Initial Coin Offerings

Abstract

While machine-generated information is increasingly prevalent, how it is used as a basis for human ratings is not well-explored. Using the context of online professional ratings of initial coin offerings (ICOs) projects, this study examines how increased social exposure of human ratings and different experiences impact human experts’ ratings relative to machine-generated ratings (MGRs). Leveraging an interface design change on an ICO rating platform, we find that increased social exposure leads experts with advisor experiences to lower ratings and rate below and closer to MGRs. Additionally, increased social exposure leads human experts with team member experiences to rate closer to MGRs, without significantly affecting their rating levels. These suggest that human experts with advisor experiences may strategically rate above MGRs to overrate and impress project teams, while those with team member experiences do not. Overall, increased social exposure drives human experts to conform to MGRs, possibly correcting humans’ rating biases.