Microsoft Research Lab – New York City
项目
Dataset and Benchmark for Interpreting STEM Articles
By understanding human decisions at a granular level, Maia can help identify a player’s strengths and weaknesses, with the goal of helping them learn and improve their gameplay.
Datamations are powerful animations that show the story of how data are sliced, sifted, and transformed on their way to the final visualization.
We take a human-centered approach to transparency, empirically studying how to provide stakeholders of ML systems with the right information to achieve their goals.
The AI Fairness Checklist’s goal is to conduct an iterative co-design process with 48 AI practitioners across 12 technology companies to understand the role of checklists in AI ethics, focusing on fairness.
Datasheets help dataset creators uncover sources of bias or hidden assumptions and help dataset consumers determine if a dataset meets their needs.
Accelerating development of machine learning applications for engineers and data scientists.
成立:
This project examines issues around AI FATE (Fairness, Accountability, Transparency, and Ethics) and Responsible AI, with a particular focus on how AI systems impact people with disabilities. This includes ensuring that mainstream AI tools are inclusively designed such that they…
Markets match individuals to scarce resources or to each other. By designing the rules and infrastructure that govern markets, we can guide participants to optimal outcomes. In the process, we circumvent a wide range of market failures. If you are…