#Fairness

Recent research from the University of California San Diego and University of Wisconsin–Madison reveals critical insights about public expectations for algorithmic decision-making in high-stakes contexts. The study, presented at the 2025 ACM CHI conference, explored how ordinary people react when multiple high-accuracy machine learning models reach different conclusions for identical applications. The findings challenge both current industry practices and academic assumptions about fair automated decision-making, with direct implications for Thailand’s rapidly expanding use of AI systems in financial services, employment, and government programs.

A new study by computer scientists at the University of California San Diego and the University of Wisconsin–Madison warns that relying on a single “best” machine learning (ML) model for high‑stakes decisions — from loan approvals to hiring — can undermine perceived fairness, and that ordinary people prefer human arbitration when equally good models disagree. The research, presented at the 2025 ACM CHI conference, explored how lay stakeholders react when multiple high‑accuracy models reach different conclusions for the same applicant and found strong resistance to both single‑model arbitrariness and to solutions that simply randomize outcomes; instead participants favored wider model searches, transparency and human decision‑making to resolve disagreements UC San Diego report and the authors’ paper Perceptions of the Fairness Impacts of Multiplicity in Machine Learning (CHI 2025) presents the detailed results and recommendations.

A recent psychological study has found that individuals with higher levels of psychopathic traits are capable of recognizing unfair treatment, but are significantly less likely to act by punishing such behavior. This groundbreaking research, recently highlighted by PsyPost, sheds new light on how personality characteristics influence responses to injustice, raising important questions for educators, mental health professionals, and policymakers in Thailand and worldwide.

Understanding the motives behind moral or antisocial behavior is critical for developing effective interventions in society. For Thai readers familiar with discussions about social justice and community well-being, these findings offer new insights into why some individuals may remain passive witnesses to unfair acts, failing to support victims or deter wrongdoers. While the classic image of the “psychopath” is associated with lack of empathy and disregard for rules, the latest findings suggest that even those with pronounced psychopathic tendencies are not necessarily unaware of what is unfair—they simply make a conscious decision not to intervene.