298 citations · 688 across the 13 of their papers we have counts for
14 papers
Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AI
Joshua A. Kroll, Andrew Smart, R. Stuart Geiger +1
As automated decision-making and data-driven technologies pervade society and are used to manage consequential outcomes, understanding the technology's capabilities, limitations, a…
Beyond the ML Model: Applying Safety Engineering Frameworks to Text-to-Image Development
Shalaleh Rismani, Renee Shelby, Andrew Smart +3
Identifying potential social and ethical risks in emerging machine learning (ML) models and their applications remains challenging. In this work, we applied two well-established sa…
Statistical Methods for Auditing the Quality of Manual Content Reviews
Xuan Yang, Andrew J Smart, Daniel Theron
Large technology firms face the problem of moderating content on their online platforms for compliance with laws and policies. To accomplish this at the scale of billions of pieces…
Walking the Walk of AI Ethics: Organizational Challenges and the Individualization of Risk among Ethics Entrepreneurs
Sanna J. Ali, Angèle Christin, Andrew Smart +1
Amidst decline in public trust in technology, computing ethics have taken center stage, and critics have raised questions about corporate ethics washing. Yet few studies examine th…
The Equitable AI Research Roundtable (EARR): Towards Community-Based Decision Making in Responsible AI Development
Jamila Smith-Loud, Andrew Smart, Darlene Neal +14
This paper reports on our initial evaluation of The Equitable AI Research Roundtable -- a coalition of experts in law, education, community engagement, social justice, and technolo…
From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML
Shalaleh Rismani, Renee Shelby, Andrew Smart +4
Inappropriate design and deployment of machine learning (ML) systems leads to negative downstream social and ethical impact -- described here as social and ethical risks -- for use…