#TechnologyPolicy

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 new era of digital inequality is emerging worldwide, as recent research reveals that artificial intelligence (AI) computing power is rapidly becoming concentrated in just a handful of countries and companies. This growing divide between AI “haves” and “have-nots” is set to disrupt economies, fuel geopolitical rivalries, and shape the scientific and social futures of entire nations, according to a major new investigation by Oxford University researchers and reporting by The New York Times (nytimes.com).

A rapidly escalating wave of academic dishonesty has gripped universities worldwide, with a recent UK study exposing a dramatic rise in students caught cheating with artificial intelligence tools like ChatGPT. The findings, which reveal nearly 7,000 proven cases of AI-facilitated cheating between 2023 and 2024, spotlight an urgent challenge for educators not just in the UK but across the globe, including Thailand. Experts warn these figures are likely just the “tip of the iceberg,” suggesting that the true scope of technology-driven misconduct is far greater and largely undetected—potentially transforming how societies view and manage academic integrity (The Guardian).

A groundbreaking study by Harvard University has found that GPT-4o, OpenAI’s latest large language model, exhibits behaviors akin to human cognitive dissonance—a psychological phenomenon previously thought to be exclusively human. The findings, reported in the prestigious Proceedings of the National Academy of Sciences on May 28, 2025, raise fresh questions about how advanced AI systems process information and make decisions, carrying significant implications for Thailand’s growing embrace of AI-driven technology in education, health, and society at large (TechXplore).

In an event reverberating across the scientific community, the Massachusetts Institute of Technology (MIT) has publicly rescinded its institutional support for a recent artificial intelligence (AI) research paper. The move, reported by Retraction Watch, raises fundamental questions about the future of AI-authored academic articles and the ethical challenges facing research institutions worldwide.

The decision by MIT, a global powerhouse in science and technology innovation, has significant implications for the credibility and accountability of scientific literature. AI-generated content, increasingly prevalent in both drafting and data analysis duties, is under the microscope for issues relating to originality, transparency, and academic integrity. For Thai academic institutions, researchers, and the broader public, this case exemplifies the urgent need to establish clear policies and ethical guardrails as the adoption of AI tools accelerates throughout the research landscape.

A groundbreaking wave of neuroscience research is redefining what it means to think—and, crucially, why artificial intelligence (AI) still falls far short of the intricacies of the human mind. A newly published feature in Salon highlights the distinct evolutionary adaptations that make the human brain more than a glorified computer, challenging decades-old assumptions fundamental to AI development and the neural network concept that underlies machine learning models.

For years, popular understanding—and much of AI research—has treated the brain as a vast network made up of nearly identical neurons whose intelligence emerges through the patterns of their collective firing. This view inspired so-called artificial neural networks, computer systems designed to solve problems by mimicking the way brains process information. Such analogies, cemented over decades, fostered the belief that if machines could imitate the structure and connectivity of brains, they might one day match, or even surpass, human intellect. But recent scientific discoveries show this metaphor misses the mark in fundamental ways, with profound implications for both neuroscience and the future of AI.