EDITORS NOTE: I (Nathan) have struggled whether to rush to comment on this proposed OMB rule. Being a recent Fiscal Year 2026 OMB memo, and knowing that the OMB will continue to create new forms of bu...

How to adapt our science funding mechanisms to the unique infrastructure needs of large-scale AI projects

Deputy Director – Data, Analytics and Infrastructure Resource - AFL-CIO : District of Columbia — Union Jobs Clearinghouse

At a time when seemingly every aspect of social life is being subjected to quantification in one way or another, “quality, not quantity” remains an omnipresent aphorism. Scholars have meticulously ...

See how Tesla CEO Elon Musk’s new payment package compares with other occupations in the United States.

In Brownsville, Texas, some families found a buzzy new school’s methods—surveillance of kids, software in lieu of teachers—to be an education in and of itself.

Background Despite the increasing implementation of artificial intelligence (AI) and machine learning (ML) technologies in healthcare, their long-term safety, effectiveness, and equity remain compromised by a lack of sustained oversight. This study explores the phenomenon of a “responsibility vacuum” in AI governance, wherein maintenance and monitoring tasks are poorly defined, inconsistently performed, and undervalued across healthcare systems. Methods We conducted semi-structured interviews with 21 experts involved in AI implementation in healthcare, including clinicians, clinical informaticists, computer scientists, and legal/policy professionals. Participants were recruited through purposive and snowball sampling. Interviews were transcribed, coded, and analyzed using abductive qualitative methods to identify themes related to maintenance practices, institutional incentives, and responsibility attribution. Results Participants widely recognized that AI models degrade over time due to factors such as data drift, changes in clinical practice, and poor generalizability. However, monitoring practices remain ad hoc and fragmented, with few institutions investing in structured oversight infrastructure. This “responsibility vacuum” is perpetuated by institutional incentives favoring rapid innovation and strategic ignorance of AI failures. Despite these challenges, some participants described grassroots efforts to monitor and maintain AI systems, drawing inspiration from fields such as radiology, laboratory medicine, and transportation safety. Conclusions Our findings suggest that institutional and cultural forces in healthcare deprioritize the maintenance of AI tools, creating a governance gap that may lead to patient harm and inequitable outcomes. Addressing this responsibility vacuum will require formalized accountability structures, interdisciplinary collaboration, and policy reforms that center long-term safety and equity. Without such changes, AI/ML technologies designed to improve patient health may introduce new forms of harm, ultimately eroding trust in AI and machine learning for healthcare.

The digitization of health care has had important consequences for how medical training is conducted, and with the advent of ambient documentation, this important social component of health care is li...

Niche researchers in a government-dependent education field are trying to pivot. “There are no crops coming out of this ground anymore.”

NIH’s sudden move to multiyear grant funding is forcing shocking cuts in the number of grants funded. This is an effective budget cut. It's bad, folks.

Power couple Maria and Kenneth Fishel will host an Adams reelection event at their Bridgehampton estate.

President Yoweri Museveni was careful not to refer to Asians as citizens; he explained that they were ‘investors’,...

This review articulates sociology's emerging approach to public administration, building on long-standing interest in bureaucracy. The sociology of public administration aims to understand how pu...