Proposes a framework separating descriptive from normative aspects of generative AI representations, arguing for plurality over a single unbiased standard.
Recommended citation: Jennifer Chien. (Forthcoming). "What Should AI Generate? Moving Beyond Bias in Generative Systems." AIES 2026.
A 50-year scoping review of Interspeech/ICASSP research reveals medicalized framings and ableist language limiting inclusion of neurodivergent speech.
Recommended citation: Rebecca Lietz, Jingjin Li, Peiyao Liu, Jennifer Chien, Norman Su, Shaomei Wu. (Forthcoming). "Making Room for Speech Diversity: A 50 Year Retrospective of Speech Science and Technology through a Neurodivergent Lens." Interspeech 2026.
Examines how Embedded Ethics curricula can inadvertently alienate CS students, and calls for more broadly relatable framings of ethical dilemmas.
Recommended citation: Louie Ortiz and Jennifer Chien. 2026. "Making Ethics Matter: Relatable Pedagogical Approaches for Every Computer Science Student." RESPECT 2026.
Expands representational harm beyond behavioral measures to cognitive and affective states, with a focus on large language models.
Recommended citation: Jennifer Chien, David Danks. (2024). "Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation." FAccT 2024.
Extends algorithmic recourse to generative language models, letting users dynamically adjust toxicity-filtering thresholds for language reclamation.
Recommended citation: Jennifer Chien, Kevin R. McKee, Jackie Kay, William Isaac. (2024). "Recourse for Reclamation: Chatting with Generative Language Models." CHI Late Breaking Work (LBW) 2024.
Argues that replacing human research participants with AI surrogates conflicts with core values of representation and inclusion. Editors' Choice at CHI 2024.
Recommended citation: William Agnew, A. Stevie Bergman, Jennifer Chien, Mark Diaz, Seliem El-Sayed, Jaylen Pittman, Shakir Mohamed, Kevin R. McKee. (2024). "The Illusion of Artificial Inclusion." CHI 2024.
Formalizes algorithmic censoring in dynamic learning systems and proposes recourse and randomized exploration as mitigations.
Recommended citation: Jennifer Chien, Margaret Roberts, Berk Ustun. (2023). "Algorithmic Censoring in Dynamic Learning Systems." EEAMO 2023.
Demonstrates GAN-generated recourse for improving engagement prediction in a maternal health training app for birth attendants.
Recommended citation: Jennifer Chien, Anna Guitart, Ana Fernández del Río, África Periáñez, Lauren Bellhouse. (2022). "Actionable Recourse via GANs for Mobile Health." ML4H 2022.
Proposes equity in epistemic utility as an alternative to conventional fairness in personalized recommender systems.
Recommended citation: Jennifer Chien, David Danks. "Fairness Vs. Personalization: Towards Equity in Epistemic Utility." Journal of Responsible Computing 2026.
Takes a philosophical view of how stochastic AI behavior challenges trust, proposing latent value modeling to assess alignment.
Recommended citation: Jennifer Chien, David Danks. "Trustworthiness in Stochastic Systems: Towards Opening the Black Box." In Submission.
A meta-analysis of 139 fairness papers reveals a US-centric, binary approach to identity, with recommendations for more inclusive framings.
Recommended citation: Jennifer Chien, A. Stevie Bergman, Kevin R. McKee, Nenad Tomasev, Vinodkumar Prabhakaran, Rida Qadri, Nahema Marchal, William Isaac. "(Unfair) Norms in Fairness Research: A Meta-Analysis." In Submission.
