Angelina Wang

According to our database, Angelina Wang authored at least 45 papers between 2017 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

Queer NLP: A Critical Survey on Literature Gaps, Biases and Trends.

Sabine Weber, Angelina Wang, Ankush Gupta, Arjun Subramonian, Dennis Ulmer, Eshaan Tanwar, Geetanjali Aich, Hannah Devinney, Jacob Hobbs, Jennifer Mickel, Joshua Tint, Mae Sosto, Ray Groshan, Simone Astarita, Vagrant Gautam, Verena Blaschke, William Agnew, Wilson Y. Lee, Yanan Long

CoRR, February, 2026

The Limits of AI Data Transparency Policy: Three Disclosure Fallacies.

Judy Hanwen Shen, Ken Liu, Angelina Wang, Sarah H. Cen, Andy K. Zhang, Caroline Meinhardt, Daniel Zhang, Kevin Klyman, Rishi Bommasani, Daniel E. Ho

CoRR, January, 2026

Ambiguity Collapse by LLMs: A Taxonomy of Epistemic Risks.

Shira Gur-Arieh, Angelina Wang, Sina Fazelpour

Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency, 2026

Scrutinizing Index-Based Risk Assessments: A Case Study in NYC Decision-making for Heat Emergency Management.

Jennah Gosciak, Luke Boyce, Angelina Wang, Allison Koenecke

Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency, 2026

SCENEBench: An Audio Understanding Benchmark Grounded in Assistive and Industrial Use Cases.

Laya Iyer, Angelina Wang, Sanmi Koyejo

Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics, 2026

Whose Knowledge Counts? Co-Designing Community-Centered AI Auditing Tools with Educators in Hawai'i.

Dora Zhao, Hannah Cha, Michael J. Ryan, Angelina Wang, Rachel Baker-Ramos, Evyn-Bree Helekahi-Kaiwi, Rebecca Diego, Josiah D. Hester, Diyi Yang

Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, 2026

2025

Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluations.

Anka Reuel, Avijit Ghosh, Jenny Chim, Andrew Tran, Yanan Long, Jennifer Mickel, Usman Gohar, Srishti Yadav, Pawan Sasanka Ammanamanchi, Mowafak Allaham, Hossein A. Rahmani, Mubashara Akhtar, Felix Friedrich, Robert Scholz, Michael Alexander Riegler, Jan Batzner, Eliya Habba, Arushi Saxena, Anastassia Kornilova, Kevin Wei, Prajna Soni, Yohan Mathew, Kevin Klyman, Jeba Sania, Subramanyam Sahoo, Olivia Beyer Bruvik, Pouya Sadeghi, Sujata S. Goswami, Angelina Wang, Yacine Jernite, Zeerak Talat, Stella Biderman, Mykel J. Kochenderfer, Sanmi Koyejo, Irene Solaiman

CoRR, November, 2025

Disclosure and Evaluation as Fairness Interventions for General-Purpose AI.

Vyoma Raman, Judy Hanwen Shen, Andy K. Zhang, Lindsey A. Gailmard, Rishi Bommasani, Daniel E. Ho, Angelina Wang

CoRR, October, 2025

The Inadequacy of Offline LLM Evaluations: A Need to Account for Personalization in Model Behavior.

Angelina Wang, Daniel E. Ho, Sanmi Koyejo

CoRR, September, 2025

Bridging Prediction and Intervention Problems in Social Systems.

Lydia T. Liu, Inioluwa Deborah Raji, Angela Zhou, Luke Guerdan, Jessica Hullman, Daniel Malinsky, Bryan Wilder, Simone Zhang, Hammaad Adam, Amanda Coston, Benjamin Laufer, Ezinne Nwankwo, Michael Zanger-Tishler, Eli Ben-Michael, Solon Barocas, Avi Feller, Marissa Gerchick, Talia Gillis, Shion Guha, Daniel E. Ho, Lily Hu, Kosuke Imai, Sayash Kapoor, Joshua Loftus, Razieh Nabi, Arvind Narayanan, Ben Recht, Juan Carlos Perdomo, Matthew J. Salganik, Mark P. Sendak, Alexander Tolbert, Berk Ustun, Suresh Venkatasubramanian, Angelina Wang, Ashia Wilson

CoRR, July, 2025

The California Report on Frontier AI Policy.

Rishi Bommasani, Scott R. Singer, Ruth E. Appel, Sarah H. Cen, A. Feder Cooper, Elena Cryst, Lindsey A. Gailmard, Ian Klaus, Meredith M. Lee, Inioluwa Deborah Raji, Anka Reuel, Drew Spence, Alexander Wan, Angelina Wang, Daniel Zhang, Daniel E. Ho, Percy Liang, Dawn Song, Joseph E. Gonzalez, Jonathan Zittrain, Jennifer Tour Chayes, Mariano-Florentino Cuellar, Li Fei-Fei

CoRR, June, 2025

Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor.

Alexandra Olteanu, Su Lin Blodgett, Agathe Balayn, Angelina Wang, Fernando Diaz, Flávio du Pin Calmon, Margaret Mitchell, Michael D. Ekstrand, Reuben Binns, Solon Barocas

CoRR, June, 2025

Measurement to Meaning: A Validity-Centered Framework for AI Evaluation.

Olawale Salaudeen, Anka Reuel, Ahmed Ahmed, Suhana Bedi, Zachary Robertson, Sudharsan Sundar, Ben Domingue, Angelina Wang, Sanmi Koyejo

CoRR, May, 2025

Toward an Evaluation Science for Generative AI Systems.

Laura Weidinger, Inioluwa Deborah Raji, Hanna M. Wallach, Margaret Mitchell, Angelina Wang, Olawale Salaudeen, Rishi Bommasani, Deep Ganguli, Sanmi Koyejo, William Isaac

CoRR, March, 2025

The inadequacy of offline large language model evaluations: A need to account for personalization in model behavior.

Angelina Wang, Daniel E. Ho, Sanmi Koyejo

Patterns, 2025

Large language models that replace human participants can harmfully misportray and flatten identity groups.

Angelina Wang, Jamie Morgenstern, John P. Dickerson

Nat. Mac. Intell., 2025

Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge.

Hanna M. Wallach, Meera A. Desai, A. Feder Cooper, Angelina Wang, Chad Atalla, Solon Barocas, Su Lin Blodgett, Alexandra Chouldechova, Emily Corvi, P. Alex Dow, Jean Garcia-Gathright, Alexandra Olteanu, Nicholas J. Pangakis, Stefanie Reed, Emily Sheng, Dan Vann, Jennifer Wortman Vaughan, Matthew Vogel, Hannah Washington, Abigail Z. Jacobs

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Measuring Machine Learning Harms from Stereotypes Requires Understanding Who Is Harmed by Which Errors in What Ways.

Angelina Wang, Xuechunzi Bai, Solon Barocas, Su Lin Blodgett

Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, 2025

Identities are not Interchangeable: The Problem of Overgeneralization in Fair Machine Learning.

Angelina Wang

Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, 2025

Fairness through Difference Awareness: Measuring Desired Group Discrimination in LLMs.

Angelina Wang, Michelle Phan, Daniel E. Ho, Sanmi Koyejo

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

2024

Operationalizing Responsible Machine Learning: From Equality Towards Equity

Angelina Wang

PhD thesis, 2024

Benchmark suites instead of leaderboards for evaluating AI fairness.

Angelina Wang, Aaron Hertzmann, Olga Russakovsky

Patterns, 2024

Evaluating Generative AI Systems is a Social Science Measurement Challenge.

Hanna M. Wallach, Meera A. Desai, Nicholas Pangakis, A. Feder Cooper, Angelina Wang, Solon Barocas, Alexandra Chouldechova, Chad Atalla, Su Lin Blodgett, Emily Corvi, P. Alex Dow, Jean Garcia-Gathright, Alexandra Olteanu, Stefanie Reed, Emily Sheng, Dan Vann, Jennifer Wortman Vaughan, Matthew Vogel, Hannah Washington, Abigail Z. Jacobs

CoRR, 2024

Measuring machine learning harms from stereotypes: requires understanding who is being harmed by which errors in what ways.

Angelina Wang, Xuechunzi Bai, Solon Barocas, Su Lin Blodgett

CoRR, 2024

Measuring Implicit Bias in Explicitly Unbiased Large Language Models.

Xuechunzi Bai, Angelina Wang, Ilia Sucholutsky, Thomas L. Griffiths

CoRR, 2024

Large language models cannot replace human participants because they cannot portray identity groups.

Angelina Wang, Jamie Morgenstern, John P. Dickerson

CoRR, 2024

Visions of a Discipline: Analyzing Introductory AI Courses on YouTube.

Severin Engelmann, Madiha Zahrah Choksi, Angelina Wang, Casey Fiesler

Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024

Strategies for Increasing Corporate Responsible AI Prioritization.

Angelina Wang, Teresa Datta, John P. Dickerson

Proceedings of the Seventh AAAI/ACM Conference on AI, Ethics, and Society (AIES-24) - Full Archival Papers, October 21-23, 2024, San Jose, California, USA, 2024

2023

Manipulative tactics are the norm in political emails: Evidence from 300K emails from the 2020 US election cycle.

Arunesh Mathur, Angelina Wang, Carsten Schwemmer, Maia Hamin, Brandon M. Stewart, Arvind Narayanan

Big Data Soc., January, 2023

Overcoming Bias in Pretrained Models by Manipulating the Finetuning Dataset.

Angelina Wang, Olga Russakovsky

CoRR, 2023

Overwriting Pretrained Bias with Finetuning Data.

Angelina Wang, Olga Russakovsky

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Gender Artifacts in Visual Datasets.

Nicole Meister, Dora Zhao, Angelina Wang, Vikram V. Ramaswamy, Ruth Fong, Olga Russakovsky

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Against Predictive Optimization: On the Legitimacy of Decision-Making Algorithms that Optimize Predictive Accuracy.

Angelina Wang, Sayash Kapoor, Solon Barocas, Arvind Narayanan

Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

Taxonomizing and Measuring Representational Harms: A Look at Image Tagging.

Jared Katzman, Angelina Wang, Morgan Klaus Scheuerman, Su Lin Blodgett, Kristen Laird, Hanna M. Wallach, Solon Barocas

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets.

Angelina Wang, Alexander Liu, Ryan Zhang, Anat Kleiman, Leslie Kim, Dora Zhao, Iroha Shirai, Arvind Narayanan, Olga Russakovsky

Int. J. Comput. Vis., 2022

Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation.

Angelina Wang, Vikram V. Ramaswamy, Olga Russakovsky

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Measuring Representational Harms in Image Captioning.

Angelina Wang, Solon Barocas, Kristen Laird, Hanna M. Wallach

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

2021

The Limits of Global Inclusion in AI Development.

Alan Chan, Chinasa T. Okolo, Zachary Terner, Angelina Wang

CoRR, 2021

Directional Bias Amplification.

Angelina Wang, Olga Russakovsky

Proceedings of the 38th International Conference on Machine Learning, 2021

Understanding and Evaluating Racial Biases in Image Captioning.

Dora Zhao, Angelina Wang, Olga Russakovsky

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020

ViBE: A Tool for Measuring and Mitigating Bias in Image Datasets.

Angelina Wang, Arvind Narayanan, Olga Russakovsky

CoRR, 2020

REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets.

Angelina Wang, Arvind Narayanan, Olga Russakovsky

Proceedings of the Computer Vision - ECCV 2020, 2020

2019

Learning Robotic Manipulation through Visual Planning and Acting.

Angelina Wang, Thanard Kurutach, Kara Liu, Pieter Abbeel, Aviv Tamar

CoRR, 2019

Learning Robotic Manipulation through Visual Planning and Acting.

Angelina Wang, Thanard Kurutach, Pieter Abbeel, Aviv Tamar

Proceedings of the Robotics: Science and Systems XV, 2019

2017

Safer Classification by Synthesis.

William Wang, Angelina Wang, Aviv Tamar, Xi Chen, Pieter Abbeel

CoRR, 2017