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.

Contact

Year

Respondents

Dataset

Variables

Availability

Felix Rebitschek, Max Planck Institut [E-Mail], Gerd Gigerenzer, Max Planck Institut [E-Mail]

2018

3086

inno

im_trustpred, trustpred_order, trustpred01b, trustpred01c, trustpred01d, trustpred01e, trustpred_random, trustpred02_split, trustpred03b, trustpred03c, trustpred03d, trustpred03e, trustpred04b, trustpred04c, trustpred04d, trustpred04e, trustpred05b, trustpred05c, trustpred05d, trustpred05e, trustpred06b, trustpred06c, trustpred06d, trustpred06e, trustpred07b, trustpred07c, trustpred07d, trustpred07e, trustpred08b, trustpred08c, trustpred08d, trustpred08e

04/2021