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Societal AI

微软研究院博客

The image shows a radar chart comparing the performance of different AI models across various metrics. The chart has a circular grid with labeled axes including VO, AS, CEc, CEe, CL, MCr, MCt, MCu, MS, QLI, QLqA, SNs, KNa, KNc, KNF, KNn, and AT. Different AI models are represented by various line styles: Babbage-002 (dotted line), Davinci-002 (dash-dotted line), GPT-3.5-Turbo (dashed line), GPT-4.0 (solid thin line), OpenAI ol-mini (solid thick line), and OpenAI o1 (solid bold line). There is a legend in the bottom left corner explaining the line styles for each model. The background transitions from blue on the left to green on the right.
微软研究院博客

| Lexin Zhou 和 Xing Xie

ADeLe, a new evaluation method, explains what AI systems are good at—and where they’re likely to fail. By breaking tasks into ability-based requirements, it has the potential to provide a clearer way to evaluate and predict AI model performance.

Xing Xie illustrated headshot
微软研究院播客

| Gretchen Huizinga 和 Xing Xie

New AI models aren’t just changing the world of research; they’re also poised to impact society. Xing Xie talks about Societal AI, a white paper that explores the changing landscape with an eye to future research and improved communication across…

Societal AI surrounded by a circle with two directional arrows in the center of a rectangle with Computer Science and a computer icon on the left with a directional arrow pointing to Social Science on the right with two avatar icons.
微软研究院博客

| Beibei Shi, Haotian Li, 和 Xing Xie

Learn about a new white paper on Societal AI, an interdisciplinary framework for guiding AI development that reflects shared human values. It presents key research challenges and emphasizes collaboration across disciplines.