2 citations · 4 across the 4 of their papers we have counts for
4 papers
Real-World AI Evaluation: How FRAME Generates Systematic Evidence to Resolve the Decision-Maker's Dilemma
Reva Schwartz, Gabriella Waters
Organizational leaders are being asked to make high-stakes decisions about AI deployment without dependable evidence of what these systems actually do in the environments they over…
CIRCLE: A Framework for Evaluating AI from a Real-World Lens
Reva Schwartz, Carina Westling, Morgan Briggs +12
This paper proposes CIRCLE, a six-stage, lifecycle-based framework to bridge the reality gap between model-centric performance metrics and AI's materialized outcomes in deployment.…
Pre-trained Speech Processing Models Contain Human-Like Biases that Propagate to Speech Emotion Recognition
Isaac Slaughter, Craig Greenberg, Reva Schwartz +1
Previous work has established that a person's demographics and speech style affect how well speech processing models perform for them. But where does this bias come from? In this w…
Towards Trustworthy Artificial Intelligence for Equitable Global Health
Hong Qin, Jude Kong, Wandi Ding +14
Artificial intelligence (AI) can potentially transform global health, but algorithmic bias can exacerbate social inequities and disparity. Trustworthy AI entails the intentional de…