Responsible Ai: Research Area — Prolific Citations Library

Discover 3 peer-reviewed studies in Responsible Ai (2023–2026). Explore research findings powered by Prolific's diverse participant panel.

This page lists 3 peer-reviewed papers in the research area of Responsible Ai in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (3 of 3)

Unpacking Human Preference for LLMs: Demographically Aware Evaluation with the HUMAINE Framework

Authors: N Petrova, A Gordon, E Blindow

Year: 2026

Published in: Open review

Institution: Prolific

Research Area: Human-centered AI evaluation, Bayesian statistics, Responsible AI, AI alignment, LLM Evaluation

Discipline: Machine Learning, Artificial Intelligence

The study introduces HUMAINE, a multidimensional evaluation framework for LLMs, revealing demographic-specific preference variations and ranking google/gemini-2.5-pro as the top-performing model with a posterior probability of 95.6%.

Methods: Multi-turn naturalistic conversations analyzed using a hierarchical Bayesian Bradley-Terry-Davidson model with post-stratification to census data, stratified across 22 demographic groups.

Key Findings: Performance of 28 LLMs across five human-centric dimensions, accounting for demographic-specific preferences.

Sample Size: 23404
Locating Risk: Task Designers and the Challenge of Risk Disclosure in RAI Content Work

Authors: A Qian, R Shaw, L Dabbish, J Suh, H Shen

Year: 2025

Published in: arXiv preprint arXiv ..., 2025 - arxiv.org

Institution: Carnegie Mellon University, University of Pittsburgh, University of Utah, Yale School of Medicine, Yale University

Research Area: Responsible AI, Content Moderation, Risk Disclosure, Worker Well-being in Human-Computer Interaction (HCI).

Discipline: Computational Social Science, Human-Computer Interaction (HCI)

The paper examines how task designers approach well-being risk disclosure in Responsible AI (RAI) content work, highlighting a need for better frameworks to communicate such risks effectively.

Methods: Interviews were conducted with 23 task designers from academic and industry sectors to gather insights on risk recognition, interpretation, and communication practices.

Key Findings: How task designers recognize, interpret, and communicate well-being risks in RAI content work.

Citations: 1

Sample Size: 23
Human-in-the-loop fairness: Integrating stakeholder feedback to incorporate fairness perspectives in responsible AI

Authors: E Taka, Y Nakao, R Sonoda, T Yokota, L Luo

Year: 2023

Published in: arXiv preprint arXiv ..., 2023 - arxiv.org

Institution: University of Glasgow, Fujitsu Limited

Research Area: Responsible AI, AI Fairness, Human-in-the-Loop (HITL)

Discipline: Artificial Intelligence, Ethics

DOI: https://doi.org/10.48550/arXiv.2312.08064

Citations: 7