Algorithmic Fairness from a Non-ideal Perspective. - Sina Fazelpour; Zachary C. Lipton (AIES, 2020)

Fair Machine Learning Under Partial Compliance. - Jessica Dai; Sina Fazelpour; Zachary C. Lipton (AIES, 2021)

Information elaboration and epistemic effects of diversity. - Daniel Steel; Sina Fazelpour; Bianca Crewe; Kinley Gillette (Synth., 2021)

Diversity and homophily in social networks. - Sina Fazelpour; Hannah Rubin (CogSci, 2022)

Homophily and Incentive Effects in Use of Algorithms. - Riccardo Fogliato; Sina Fazelpour; Shantanu Gupta; Zachary C. Lipton; David Danks (CogSci, 2022)

Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harms. - Terrence Neumann; Maria De-Arteaga; Sina Fazelpour (FAccT, 2022)

Diversity in sociotechnical machine learning systems. - Sina Fazelpour; Maria De-Arteaga (Big Data Soc., 2022)

Diverse, but Divisive: LLMs Can Exaggerate Gender Differences in Opinion Related to Harms of Misinformation. - Terrence Neumann; Sooyong Lee; Maria De-Arteaga; Sina Fazelpour; Matthew Lease (CoRR, 2024)

Disciplining deliberation: a sociotechnical perspective on machine learning trade-offs. - Sina Fazelpour (CoRR, 2024)

Authenticity and exclusion: social media recommendation algorithms and the dynamics of belonging in professional networks. - Nil-Jana Akpinar; Sina Fazelpour (CoRR, 2024)

Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina. - Yuan Gao; Dokyun Lee; Gordon Burtch; Sina Fazelpour (CoRR, 2024)

The Value of Disagreement in AI Design, Evaluation, and Alignment. - Sina Fazelpour; Will Fleisher (FAccT, 2025)

Should you use LLMs to simulate opinions? Quality checks for early-stage deliberation. - Terrence Neumann; Maria De-Arteaga; Sina Fazelpour (CoRR, 2025)

Aspirational Affordances of AI. - Sina Fazelpour; Meica Magnani (CoRR, 2025)