Human Ai Alignment: Research Area — Prolific Citations Library

Discover 3 peer-reviewed studies in Human Ai Alignment (2024–2026). Explore research findings powered by Prolific's diverse participant panel.

This page lists 3 peer-reviewed papers in the research area of Human Ai Alignment 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
To Mask or to Mirror: Human-AI Alignment in Collective Reasoning

Authors: C Qian, AT Parisi, C Bouleau, V Tsai

Year: 2025

Published in: Proceedings of the ..., 2025 - aclanthology.org

Institution: Google, Google DeepMind

Research Area: Human-AI Alignment, Collective Reasoning, Social Biases, LLM Simulation of Human Behavior, AI Bias

Discipline: Natural Language Processing, Artificial Intelligence, Computational Social Science

This study examines human-AI alignment in collective reasoning using an empirical framework, demonstrating how LLMs either mirror or mask human biases depending on context, cues, and model-specific inductive biases.

Methods: The study uses the Lost at Sea social psychology task in a large-scale online experiment, simulating LLM groups conditioned on human decision-making data across varying conditions of visible or pseudonymous demographics.

Key Findings: Alignment of LLM behavior with human social reasoning, focusing on collective decision-making and biases in group interactions.

Citations: 1

Sample Size: 748
Whose preferences? differences in fairness preferences and their impact on the fairness of AI utilizing human feedback

Authors: M Lerner, F Dorner, E Ash, N Goel

Year: 2024

Published in: ... of the 62nd Annual Meeting of ..., 2024 - aclanthology.org

Institution: ETH Zürich, University of Oxford

Research Area: Fairness in AI, Content Moderation, Human-AI Alignment

Discipline: Computational Social Science

Citations: 5