Fostering appropriate reliance on AI @MicrosoftResearch

Microsoft Research | Fostering appropriate reliance on AI @MicrosoftResearch | Uploaded September 2024 | Updated October 2024, 1 week ago.
Mihaela Vorvoreanu, Director UX Research and Responsible AI Education, Microsoft Aether, discusses reliance on AI. Because of their probabilistic nature, all AI systems will make mistakes. One of the main challenges in human-AI interaction is to foster appropriate reliance on AI, and empower users of AI systems to determine when to accept or not accept an AI system's recommendation. Hear about the work being done at Microsoft to foster appropriate reliance and help people accept AI outputs when they are correct, and reject them when they are wrong.

Microsoft Research Forum, September 3, 2024

See more at https://aka.ms/ResearchForum-Sep2024

Wildlife Conflict Resolution: Boma & Cattle Detection in the Masai Mara using AI

Research Forum: Closing Remarks and Announcements

Effective Human-AI Decision-Making or Everyone: A Sisyphean Task?

Getting Modular with Language Models: Building, Reusing a Library of Experts for Task Generalization

$Optimization from Structured Samples for Coverage and Influence Functions 2022 Data-driven Optimization Workshop: Optimization from Structured Samples for Coverage and Influence Functions Speaker: Zhijie Zhang, Fuzhou University We revisit the optimization from samples (OPS) model, which studies the problem of optimizing objective functions directly from the sample data. Previous results showed that we cannot obtain a constant approximation ratio for the maximum coverage problem using polynomial independent samples of the form {S_i,f(S_i )}_(i=1)^t (BRS, STOC17), even if coverage functions are (1-ϵ)-PMAC learnable using these samples (BDF+, SODA12). In this work, to circumvent the impossibility result of OPS, we propose a stronger model called optimization from structured samples (OPSS), where the data samples encode the structural information of the functions. We show that under OPSS model, the maximum coverage problem enjoys constant approximation under mild assumptions on the sample distribution. We further generalize the result and show that influence maximization also enjoys constant approximation under this model.$

Evaluation and Understanding of Foundation Models

Fighting the Global Social Media Infodemic: from Fake News to Harmful Content

Final intern talk: Distilling Self-Supervised-Learning-Based Speech Quality Assessment into Compact

AI Forum 2023 | The Emergence of General AI for Medicine

End-to-end Reinforcement Learning for the Large-scale Traveling Salesman Problem

Online Facility Location with Predictions