175 citations · 262 across the 8 of their papers we have counts for
8 papers
Introducing v0.5 of the AI Safety Benchmark from MLCommons
Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed +97
This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safe…
Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Application
Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky +2
Trust is an important factor in people's interactions with AI systems. However, there is a lack of empirical studies examining how real end-users trust or distrust the AI system th…
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction
Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky +2
Despite the proliferation of explainable AI (XAI) methods, little is understood about end-users' explainability needs and behaviors around XAI explanations. To address this gap and…
Weaving Privacy and Power: On the Privacy Practices of Labor Organizers in the U.S. Technology Industry
Sayash Kapoor, Matthew Sun, Mona Wang +2
We investigate the privacy practices of labor organizers in the computing technology industry and explore the changes in these practices as a response to remote work. Our study is…
A relationship and not a thing: A relational approach to algorithmic accountability and assessment documentation
Jacob Metcalf, Emanuel Moss, Ranjit Singh +2
Central to a number of scholarly, regulatory, and public conversations about algorithmic accountability is the question of who should have access to documentation that reveals the…
Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups
Amy A. Winecoff, Elizabeth Anne Watkins
Scholars and industry practitioners have debated how to best develop interventions for ethical artificial intelligence (AI). Such interventions recommend that companies building an…
The four-fifths rule is not disparate impact: a woeful tale of epistemic trespassing in algorithmic fairness
Elizabeth Anne Watkins, Michael McKenna, Jiahao Chen
Computer scientists are trained to create abstractions that simplify and generalize. However, a premature abstraction that omits crucial contextual details creates the risk of epis…
Qualitative Analysis for Human Centered AI
Orestis Papakyriakopoulos, Elizabeth Anne Watkins, Amy Winecoff +2
Human-centered artificial intelligence (AI) posits that machine learning and AI should be developed and applied in a socially aware way. In this article, we argue that qualitative…