Consumers’ Beliefs and Trust in Predictive Analytics

Short Description: Error estimations and error tolerances in Germany are studied with regards to algorithmic decision-making systems. We examine what people expect about the accuracy of algorithms that predict credit default, recidivism of an offender, suitability of a job applicant, and health behavior. Also, we investigate whether expectations about algorithm errors vary between these domains and how they differ from expectations about errors made by human experts.

Experimental Details: Random assignment of participants to one of two different groups (between-subjects design), assessing participants’ requirements concerning experts’ vs algorithms’ performance. One group answered questions on error tolerance regarding algorithmic predictions made by computer programs, while the other group answered questions on error tolerance regarding experts’ predictions.

Available Papers

• Rebitschek, Felix G., Gerd Gigerenzer, and Gert G. Wagner. 2021. People underestimate the errors made by algorithms for credit scoring and recidivism prediction but accept even fewer errors. Scientific Reports 11, 20171. (https://doi.org/10.1038/s41598-021-99802-y) (Pre-published 2021: People underestimate the errors by algorithms for credit scoring and recidivism but tolerate even fewer errors. PsyArXiv Preprint.).

Contact	Syear	Respondents	Dataset	Variables	Availability	Field	Method	Replication
Felix Rebitschek (Max Planck Institut), Gerd Gigerenzer (Max Planck Institut)	2018	3086	inno	trustpred06e, trustpred01c, trustpred07c, trustpred01b, trustpred04d, trustpred03d, trustpred08b, trustpred_random, trustpred04b, trustpred08e, trustpred05b, im_trustpred, trustpred02_split, trustpred05d, trustpred03e, trustpred04e, trustpred07e, trustpred05c, trustpred01e, trustpred03c, trustpred07b, trustpred06c, trustpred05e, trustpred08c, trustpred07d, trustpred03b, trustpred08d, trustpred01d, trustpred06b, trustpred06d, trustpred_order, trustpred04c	04/2021	Psychology	Experiment