430 citations · 687 across the 13 of their papers we have counts for
5 papers · 1 filter
The Malicious Technical Ecosystem: Exposing Limitations in Technical Governance of AI-Generated Non-Consensual Intimate Images of Adults
Michelle L. Ding, Harini Suresh
In this paper, we adopt a survivor-centered approach to locate and dissect the role of sociotechnical AI governance in preventing AI-Generated Non-Consensual Intimate Images (AIG-N…
"Ownership, Not Just Happy Talk": Co-Designing a Participatory Large Language Model for Journalism
Emily Tseng, Meg Young, Marianne Aubin Le Quéré +2
Journalism has emerged as an essential domain for understanding the uses, limitations, and impacts of large language models (LLMs) in the workplace. News organizations face diverge…
Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs
Harini Suresh, Kathleen M. Lewis, John V. Guttag +1
Interpretability methods aim to help users build trust in and understand the capabilities of machine learning models. However, existing approaches often rely on abstract, complex v…
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs
Harini Suresh, Steven R. Gomez, Kevin K. Nam +1
To ensure accountability and mitigate harm, it is critical that diverse stakeholders can interrogate black-box automated systems and find information that is understandable, releva…
Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making
Harini Suresh, Natalie Lao, Ilaria Liccardi
ML decision-aid systems are increasingly common on the web, but their successful integration relies on people trusting them appropriately: they should use the system to fill in gap…