Conformity Assessment, Fairness Metrics or Transparency? Users’ Perceptions on Fairness of AI Algorithms

This Dataverse contains datasets associated with the research study "Conformity Assessment, Fairness Metrics, or Transparency? Users’ Perceptions on Fairness of AI Algorithms." The study investigates how different attributes of AI decision-making systems, such as transparency, conformity assessment, and fairness metrics, influence user perceptions of fairness across various decision contexts, including hiring, credit scoring, and recidivism prediction. The data collected through Adaptive Choice-Based Conjoint (ACBC) analysis provides valuable insights into user preferences and the trade-offs they are willing to make between these attributes. The Dataverse includes the following datasets: Raw Data: Contains the raw data from the online survey, including response metadata, page time data, context information, survey responses, control variables, and quiz question data. ACBC Counts: Provides detailed counts and summaries of respondent preferences across different attributes and levels, along with individual-level data and level frequencies. ACBC Test Design: Documents the test run of the study design, including information about level appearances, standard errors, warnings, and the d-efficiency of the design. Utility Report Main Analysis: Contains utility analysis results estimated using the Hierarchical Bayes method, including average utility values, importances, and histogram data for both the overall sample and individual respondents. Utility Report Otter's Method: Provides utility analysis results estimated using the Task-Specific Scale Factors Hierarchical Bayes method (Otter’s method) to assess the robustness of the findings, with data similar to the Main Analysis utility report.