Human Machine Interaction: Research Area — Prolific Citations Library

Discover 2 peer-reviewed studies in Human Machine Interaction (2024–2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 2 peer-reviewed papers in the research area of Human Machine Interaction in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (2 of 2)

On Human Factors in Machine Fairness: Essays in Behavioral Economics

Authors: AC Wömmel

Year: 2025

Published in: 2025 - ediss.sub.uni-hamburg.de

Institution: University of Hamburg

Research Area: Machine Fairness, Behavioral Economics, Human-Machine Interaction

Discipline: Behavioral Economics, Artificial Intelligence, Human-Machine Interaction

Human behavior can undermine fairness interventions in AI, amplify inequalities, and reinforce socioeconomic disparities, emphasizing the need to integrate behavioral mechanisms into AI governance.

Methods: The dissertation utilized three empirical approaches: (i) deliberation experiments with UK participants using NLP analysis, (ii) online hiring experiments testing algorithmic fairness interventions, and (iii) panel data analysis of German households measuring digital skills and confidence levels.

Key Findings: Public attitudes towards AI, the adoption of fairness interventions in algorithmic tools, and socioeconomic disparities in digital skills and confidence.

Citations: 1
Social preferences toward humans and machines: a systematic experiment on the role of machine payoffs

Authors: A von Schenk, V Klockmann

Year: 2024

Published in: ... on Psychological Science, 2025 - journals.sagepub.com

Institution: Max Planck Institute

Research Area: Social Preferences, Behavioral Economics, Human-Machine Interaction

Discipline: Behavioral Science

Humans exhibit stronger social preferences toward machines when they know machine payoffs benefit a human recipient, and weak preferences when payoff information is absent, suggesting belief formation is self-serving.

Methods: Conducted an online experiment with participants and follow-up surveys to compare the impact of different implementations of machine payoffs and information transparency on social preferences.

Key Findings: Social preferences and reciprocity behaviors toward machines with varying payoff structures and transparency about the beneficiaries.

DOI: https://doi.org/10.1177/17456916231194949

Citations: 40

Sample Size: 1198