265 citations · 290 across the 4 of their papers we have counts for
5 papers
Developing a Risk Identification Framework for Foundation Model Uses
David Piorkowski, Michael Hind, John Richards +1
As foundation models grow in both popularity and capability, researchers have uncovered a variety of ways that the models can pose a risk to the model's owner, user, or others. Des…
AI Risk Atlas: Taxonomy and Tooling for Navigating AI Risks and Resources
Frank Bagehorn, Kristina Brimijoin, Elizabeth M. Daly +17
The rapid evolution of generative AI has expanded the breadth of risks associated with AI systems. While various taxonomies and frameworks exist to classify these risks, the lack o…
Business (mis)Use Cases of Generative AI
Stephanie Houde, Vera Liao, Jacquelyn Martino +5
Generative AI is a class of machine learning technology that learns to generate new data from training data. While deep fakes and media-and art-related generative AI breakthroughs…
Experiences with Improving the Transparency of AI Models and Services
Michael Hind, Stephanie Houde, Jacquelyn Martino +4
AI models and services are used in a growing number of highstakes areas, resulting in a need for increased transparency. Consistent with this, several proposals for higher quality…
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Rachel K. E. Bellamy, Kuntal Dey, Michael Hind +15
Fairness is an increasingly important concern as machine learning models are used to support decision making in high-stakes applications such as mortgage lending, hiring, and priso…