21 citations · 33 across the 7 of their papers we have counts for
8 papers
Ambiguity Collapse by LLMs: A Taxonomy of Epistemic Risks
Shira Gur-Arieh, Angelina Wang, Sina Fazelpour
Large language models (LLMs) are increasingly used to make sense of ambiguous, open-textured, value-laden terms. Platforms routinely rely on LLMs for content moderation, asking the…
Should you use LLMs to simulate opinions? Quality checks for early-stage deliberation
Terrence Neumann, Maria De-Arteaga, Sina Fazelpour
The emergent capabilities of large language models (LLMs) have prompted interest in using them as surrogates for human subjects in opinion surveys. However, prior evaluations of LL…
Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina
Yuan Gao, Dokyun Lee, Gordon Burtch +1
Recent studies suggest large language models (LLMs) can exhibit human-like reasoning, aligning with human behavior in economic experiments, surveys, and political discourse. This h…
Authenticity and exclusion: social media algorithms and the dynamics of belonging in epistemic communities
Nil-Jana Akpinar, Sina Fazelpour
Recent philosophical work has explored how the social identity of knowers influences how their contributions are received, assessed, and credited. However, a critical gap remains r…
Disciplining Deliberation: A Sociotechnical Perspective on Machine Learning Trade-offs
Sina Fazelpour
This paper examines two prominent formal trade-offs in artificial intelligence (AI) -- between predictive accuracy and fairness, and between predictive accuracy and interpretabilit…
Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harms
Terrence Neumann, Maria De-Arteaga, Sina Fazelpour
Faced with the scale and surge of misinformation on social media, many platforms and fact-checking organizations have turned to algorithms for automating key parts of misinformatio…
Diversity in Sociotechnical Machine Learning Systems
Sina Fazelpour, Maria De-Arteaga
There has been a surge of recent interest in sociocultural diversity in machine learning (ML) research, with researchers (i) examining the benefits of diversity as an organizationa…
Algorithmic Fairness from a Non-ideal Perspective
Sina Fazelpour, Zachary C. Lipton
Inspired by recent breakthroughs in predictive modeling, practitioners in both industry and government have turned to machine learning with hopes of operationalizing predictions to…