#EthicalAI

A multi-experiment psychology study finds that interacting with autonomous agents that display socio-emotional skills can make people judge those machines as more humanlike — and, worryingly, judge other humans as less human and more acceptable to mistreat. The research, published in the Journal of Consumer Psychology and available via the London School of Economics repository, uses five controlled experiments to show a chain from perceiving emotional ability in AI to lower “humanness” ratings of people, and finally to real choices that disadvantage human workers (e.g., preferring a company linked with poor working conditions or withholding a small donation to support staff) (PsyPost coverage; study PDF; journal record).

Groundbreaking psychological research has uncovered an unsettling paradox at the heart of artificial intelligence development. When machines display emotional intelligence that closely mirrors human capabilities, people begin viewing actual humans as less worthy of compassion and humane treatment. This phenomenon, documented through five rigorous experiments published in the Journal of Consumer Psychology, poses immediate risks for Thailand’s service-dependent economy, where millions of workers rely on human emotional connection for their livelihoods.

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.

Recent global advancements in artificial intelligence (AI) are upending assumptions about the future of computer science education, challenging educators, policymakers, and technology leaders across Thailand to elevate training standards and focus on foundational skills over rote learning. Contrary to fears that AI tools might eliminate the need for coding or computer science training, research and initiatives unfolding in Thailand and beyond suggest that AI will instead prompt a systemic overhaul—raising the bar for quality instruction and empowering both teachers and students with potent new capabilities.

Amidst the rise of artificial intelligence, a recent thought-provoking essay has sparked global discussion about the transformation of learning in the age of ChatGPT and similar technologies. As AI becomes deeply woven into everyday life and education, some experts warn that schools—whether intentionally or not—are acting more like vocational training grounds, emphasizing task completion through technological shortcuts rather than the cultivation of critical thinking, original expression, and holistic intellectual growth. This trend, experts suggest, may have profound implications for Thai students, educators, and society at large (Psychology Today).