%0 Generic
%A Yurrita Semperena, Mireia 
%A Verma, Himanshu
%A Balayn, Agathe
%A Gadiraju, Ujwal
%A Pont, Sylvia
%A Bozzon, Alessandro
%D 2025
%T Data underlying the paper "Towards Effective Human Intervention in Algorithmic Decision-Making"
%U 
%R 10.4121/8c19bb03-14de-4c85-b781-33eed0cac44a.v1
%K algorithmic decision-making
%K human intervention
%K ability
%K benevolence
%K integrity
%K fairness perceptions
%X <p>This project captures perceptions of ability, benevolence, integrity and fairness towards different decision-maker configurations in algorithmic decision-making for policy enforcement. It consists of two studies. In the first study, we interview 21 participants with experience renting out their properties out as short-term rentals. Through these interviews we identify the main characteristics that interviewees considered when evaluating the appropriateness of decision-maker configurations (i.e., decision-maker profile, model type, data provenance). In the second study, we run a crowdsourced user study in Prolific with 223 participants. They were shown an illegal holiday identification scenario. Each participant was assigned to a scenario with a decision-maker configuration with varying decision-maker profile, different model types, and data provenance. The dataset includes:</p><p><br></p><ol><li>The prompts used for running the interviews</li><li>The interview protocol</li><li>Pre-registration of the crowdsourced study</li><li>The materials used for designing the crowdsourced user study</li><li>The (anonymized) data from the crowdsourced study</li><li>The script used to analyze the crowdsourced data</li><li>A document with all the rationales behind the statistical analysis of the crowdsourced data</li><li>Analysis of open-ended questions of the crowdsourced study</li></ol>
%I 4TU.ResearchData