This work model the provider as an adversary whose instrument is effort and expresses fairness as a constraint requiring that leaving never cost more than joining, and proves that this constraint holds for every assignment of effort weights if and only if no component of the exit flow exceeds its counterpart at entry.
Oluwadamilola Awakan, Tawan Aroonwechkul, Roshan Gunjoor et al.· 0 citations
This work examines post-mortem digital governance as a sociotechnical problem of cooperative and contested work, focusing on who acts when the user is gone, what claims they make, and what barriers shape recovery, preservation, closure, and protection.
This work investigated how interviewers experience AI assistance for probing during semi-structured interviews and proposed three implications for AI-assisted human-to-human interaction: managing social pressure, balancing idea alignment with inspiration, and preserving interpersonal presence.
The Epstein Files Engine, an A.I. agent The New York Times deployed to investigate Jeffrey Epstein's files, is described and it is argued that newsroom agents serve newsrooms best not as autonomous writers, but as interfaces to source material and institutional knowledge.
D. K. Nguyen, T. M. Terol, Dylan Freedman et al.· 0 citations
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Skill Profiling with Attributable Reasoning (SPAR), an eight-IMU garment and pressure-insole system that classifies each punch as expert or novice and treats an explanation of that prediction as feedback, is presented.
Nibraas Khan, Hanchen David Wang, E. Bullard et al.· 0 citations
A single-user case study of InMyStyle, a privacy-first, single-user system that adapts small language models to rewrite AI-edited text towards an individual user's writing style without an instruction prompt at inference, finds gains favor content-preserving naturalization more than recovery of personal style.
This paper describes how the three layers fit together and a working Flutter prototype on Android that combines them with an ephemeral cloud agent invoked only when the user asks, and a working Flutter prototype on Android that combines them with an ephemeral cloud agent invoked only when the user asks.
Drawing on daily individual reflections, group reflections, and post-camp interviews, it is found that teachers' adaptive practices of repair, differentiation, translation, and balancing sit at the intersection of three tensions (technology, learner, and instruction).
Fasika Melese, Rui-Yang Wu, Xin-Yue Cui et al.· 0 citations
AI is becoming increasingly integrated into daily workflows, especially in computing. We are gradually shifting towards an AI-rich future, an impending yet unknown one. One important emerging concern is whether we are accordingly preparing our future computing workforce. Further, we need to know what the important cogn...
Neha Rani, Vu Minh Anh Le, Austin M. Spangler et al.· 0 citations
SustainAI provides a practical foundation for integrating ethical care and environmental responsibility into AI infrastructure design and lifecycle management, framing AI sustainability around relational ethics, regional equity, and ecological stewardship.
Farnaz Farid, Tashfia Towkee, S. Nasreen et al.· 0 citations
Everyday Extended Reality (XR) systems aim to provide context-aware access to the right functionalities at the right time and place, with minimal manual reconfiguration as users switch context. Yet these interfaces are hard to evaluate: current prototyping and user-study workflows offer no systematic, repeatable way to...
Hyunsung Cho, Sarah Yewon Yun, Nancy Ruonan Sun et al.· 0 citations
The present study profiled eight Serbian participants through a questionnaire, a deep interview, and a written self-presentation, recorded their reactions to sixty-eight social media posts, and asked four language models to predict those reactions under five prompt conditions varying profile content and instruction sty...
Ljubiša Bojić, Tijana Stanić, Joerg Matthes et al.· 0 citations
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduSep 30, 2026
Able to defeat top-ranked human players and more efficient than other models, the new system could help decision-makers in military maneuvers or business negotiations.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
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