Algorithmic Decision Making: Research Area — Prolific Citations Library

Discover 4 peer-reviewed studies in Algorithmic Decision Making (2022–2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 4 peer-reviewed papers in the research area of Algorithmic Decision Making in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (4 of 4)

Dimensions of diversity in human perceptions of algorithmic fairness

Authors: N Grgić-Hlača, G Lima, A Weller

Year: 2025

Published in: Proceedings of the 2nd ..., 2022 - dl.acm.org

Institution: Max Planck Institute, École Polytechnique Fédérale de Lausanne, University of Cambridge, The Alan Turing Institute

Research Area: Algorithmic Fairness, Human Perception, Diversity in AI Decision-Making

Discipline: Social Science, Artificial Intelligence

This study examines how sociodemographic factors and personal experience influence perceptions of fairness in algorithmic decision-making, particularly in bail decisions, highlighting the importance of diverse perspectives in regulatory oversight.

Methods: Explored perceptions of procedural fairness using surveys to assess the influence of demographics and personal experiences.

Key Findings: Impact of demographics (age, education, gender, race, political views) and personal experience on perceptions of fairness of algorithmic feature use in bail decisions.

DOI: 10.1145/3551624.3555306

Citations: 62
Fairness Perceptions in Regression-based Predictive Models

Authors: Mukund Telukunta, Venkata Sriram Siddhardh Nadendla, Morgan Stuart, Casey Canfield

Year: 2025

Published in: ArXiv

Institution: Missouri University of Science and Technology, United Network for Organ Sharing

Research Area: Algorithmic Fairness, Healthcare AI, Decision-Making

Discipline: Artificial Intelligence

The study investigates fairness in regression-based predictive models for kidney transplantation, introducing three group fairness notions and identifying social preferences for fairness criteria, revealing biases against age groups but fairness towards gender and race groups.

Methods: Three novel fairness notions (independence, separation, sufficiency) were introduced alongside crowd feedback analysis through a Mixed-Logit discrete choice model.

Key Findings: Fairness in regression-based predictive analytics regarding group fairness criteria across social dimensions such as age, gender, and race.

Sample Size: 85
Clinicians diagnosing virtual patients with the classification algorithm for chronic pain in the ICD-11 (CAL-CP) achieve better diagnoses and prefer the algorithm to standard tools: An experimental validation study

Authors: G Hay, Beatrice Korwisi, Norman Lahme-Hütig, Winfried Rief, Antonia Barke

Year: 2024

Published in: Wiley

Institution: Marburg University, Münster School of Business, University of Duisburg-Essen

Research Area: ICD-11 Chronic Pain Classification, Clinical Diagnosis, Algorithmic Decision-Making

Discipline: Health, Medicine, Artificial Intelligence
Taking advice from (dis) similar machines: the impact of human-machine similarity on machine-assisted decision-making

Authors: N Grgić-Hlača, C Castelluccia

Year: 2022

Published in: Proceedings of the AAAI ..., 2022 - ojs.aaai.org

Institution: École Polytechnique Fédérale de Lausanne (EPFL), Inria

Research Area: Human-Computer Interaction (HCI), Algorithmic Decision-Making, Human-AI Collaboration

Discipline: Artificial Intelligence

DOI: https://doi.org/10.1609/hcomp.v10i1.21989

Citations: 22