26 citations · 34 across the 8 of their papers we have counts for
8 papers
Improving governance outcomes through AI documentation: Bridging theory and practice
Amy A. Winecoff, Miranda Bogen
Documentation plays a crucial role in both external accountability and internal governance of AI systems. Although there are many proposals for documenting AI data, models, systems…
Techno-Utopians, Scammers, and Bullshitters: The Promise and Peril of Web3 and Blockchain Technologies According to Operators and Venture Capital Investors
Amy A. Winecoff, Johannes Lenhard
Proponents and developers of Web3 and blockchain argue that these technologies can revolutionize how people live and work by empowering individuals and distributing decision-making…
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…
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…
Simulation as Experiment: An Empirical Critique of Simulation Research on Recommender Systems
Amy A. Winecoff, Matthew Sun, Eli Lucherini +1
Simulation can enable the study of recommender system (RS) evolution while circumventing many of the issues of empirical longitudinal studies; simulations are comparatively easier…
T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systems
Eli Lucherini, Matthew Sun, Amy Winecoff +1
Simulation has emerged as a popular method to study the long-term societal consequences of recommender systems. This approach allows researchers to specify their theoretical model…
Recommendation or Discrimination?: Quantifying Distribution Parity in Information Retrieval Systems
Rinat Khaziev, Bryce Casavant, Pearce Washabaugh +2
Information retrieval (IR) systems often leverage query data to suggest relevant items to users. This introduces the possibility of unfairness if the query (i.e., input) and the re…
Assessing Fashion Recommendations: A Multifaceted Offline Evaluation Approach
Jake Sherman, Chinmay Shukla, Rhonda Textor +2
Fashion is a unique domain for developing recommender systems (RS). Personalization is critical to fashion users. As a result, highly accurate recommendations are not sufficient un…