Angelina Wang

According to our database, Angelina Wang authored at least 20 papers between 2017 and 2024.

Collaborative distances:

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

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

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