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• Incorporating Procedural Fairness in Flag Submissions on Social Media Platforms

  Yunhee Shim, Shagun Jhaver · 2026 · ACM Transactions on Social Computing

  This paper examines how the design choices embedded in social media flagging mechanisms shape users' perceptions of procedural fairness. Flagging — the ability for users to report posts or accounts that violate community guidelines — is a critical component of platform…

  content moderation · platform governance · procedural fairness · flagging · social media

• Policy Maps: Tools for Guiding the Unbounded Space of LLM Behaviors

  Michelle S. Lam, Fred Hohman, Dominik Moritz, Jeffrey P. Bigham, Kenneth Holstein, Mary Beth Kery · 2025 · Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology (UIST '25)

  This paper introduces "policy maps," an approach to AI policy design for large language models inspired by physical mapmaking. The core insight is that comprehensive policy coverage over an unbounded space of LLM inputs and outputs is impossible — just as no map can capture…

  AI safety · AI policy · large language models · AI ethics · model evaluation

• Nothing Micro About It: Examining Ableist Microaggressions on Social Media

  Sharon Heung, Mahika Phutane, Shiri Azenkot, Megh Marathe, Aditya Vashistha · 2022 · Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '22)

  This paper presents the first study examining how disabled people experience ableist microaggressions specifically on social media. The researchers conducted semi-structured interviews with 20 participants with various disabilities — including physical disabilities, visual…

  ableism · microaggressions · social media · disability identity · content moderation

• Unintended Machine Learning Biases as Social Barriers for Persons with Disabilities

  Ben Hutchinson, Vinodkumar Prabhakaran, Remi Denton, Kellie Webster, Yu Zhong, Stephen Denuyl · 2020 · SIGACCESS Accessibility and Computing

  This paper from Google's Ethical AI team provides concrete empirical evidence that widely deployed NLP models encode measurable biases against people with disabilities, creating social barriers through technology. The authors examine three layers of bias using a perturbation…

  machine learning bias · NLP · toxicity detection · sentiment analysis · disability representation

4 results.