12 papers
FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data
Nuredin Ali Abdelkadir, Anjali Ratnam, Zeerak Talat +1
Social media text data are often used to train Machine Learning (ML) models to identify users exhibiting high-risk mental health behaviors. However, sharing this sensitive data pos…
Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluations
Anka Reuel, Avijit Ghosh, Jenny Chim +32
Foundation models are increasingly central to high-stakes AI systems, and governance frameworks now depend on evaluations to assess their risks and capabilities. Although general c…
Big AI's Regulatory Capture: Mapping Industry Interference and Government Complicity
Abeba Birhane, Riccardo Angius, William Agnew +4
Over the past decade, the AI industry has come to exert an unprecedented economic, political and societal power and influence. It is therefore critical that we comprehend the exten…
Aligning Stuttered-Speech Research with End-User Needs: Scoping Review, Survey, and Guidelines
Hawau Olamide Toyin, Mutiah Apampa, Toluwani Aremu +6
Atypical speech is receiving greater attention in speech technology research, but much of this work unfolds with limited interdisciplinary dialogue. For stuttered speech in particu…
Who Gets Heard? Rethinking Fairness in AI for Music Systems
Atharva Mehta, Shivam Chauhan, Megha Sharma +5
In recent years, the music research community has examined risks of AI models for music, with generative AI models in particular, raised concerns about copyright, deepfakes, and tr…
Online Learning Defense against Iterative Jailbreak Attacks via Prompt Optimization
Masahiro Kaneko, Zeerak Talat, Timothy Baldwin
Iterative jailbreak methods that repeatedly rewrite and input prompts into large language models (LLMs) to induce harmful outputs -- using the model's previous responses to guide e…