#Banking

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 from researchers at the University of California San Diego and the University of Wisconsin–Madison, presented at the 2025 ACM CHI conference, examines how the public wants decisions made when multiple high-accuracy AI models disagree. The findings are especially relevant to Thailand as AI use grows in finance, employment, and government services.

The study centers on multiplicity—the reality that many models can achieve similar accuracy but still produce different predictions for the same case. This raises ethical questions for organizations choosing which model to deploy, particularly for loans, jobs, or social services. Data resonates with Thailand’s push to sharpen AI risk management guidelines in finance, signaling regulators’ attention to fairness in automated decisions.

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.