Data underlying the paper "Disentangling Fairness Perceptions in Algorithmic Decision-Making: the Effects of Explanations, Human Oversight, and Contestability"

This projects captures fairness perceptions towards algorithmic decision-making processes with varying levels of explanations, human oversight, and contestability. 267 people participated in a crowdsourced study via Prolific. They were shown a loan approval scenario where an individual would get their loan request rejected. Each participant was assigned to a scenario with or without explanation, with or without human oversight, and with or without the right to contest the decision. The dataset includes:

1. The materials used for designing the study
2. The preregistration of the study
3. The (anonymized) data
4. The script used to analyze the data