Angelina Wang

868 total citations · 2 hit papers
19 papers, 349 citations indexed

About

Angelina Wang is a scholar working on Artificial Intelligence, Safety Research and Computer Vision and Pattern Recognition. According to data from OpenAlex, Angelina Wang has authored 19 papers receiving a total of 349 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 7 papers in Safety Research and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Angelina Wang's work include Ethics and Social Impacts of AI (7 papers), Explainable Artificial Intelligence (XAI) (5 papers) and Multimodal Machine Learning Applications (4 papers). Angelina Wang is often cited by papers focused on Ethics and Social Impacts of AI (7 papers), Explainable Artificial Intelligence (XAI) (5 papers) and Multimodal Machine Learning Applications (4 papers). Angelina Wang collaborates with scholars based in United States, United Kingdom and Germany. Angelina Wang's co-authors include Olga Russakovsky, V. Ramaswamy, Solon Barocas, Arvind Narayanan, Pieter Abbeel, Aviv Tamar, Thanard Kurutach, Hanna Wallach, Sayash Kapoor and John P. Dickerson and has published in prestigious journals such as Proceedings of the National Academy of Sciences, PLoS ONE and International Journal of Computer Vision.

In The Last Decade

Angelina Wang

19 papers receiving 332 citations

Hit Papers

Large language models that replace human participants can... 2025 2026 2025 2025 5 10 15 20
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Large language models that replace human participants can harmfully misportray and flatten identity groups
    2025 20 citations Angelina Wang, Jamie Morgenstern et al. Nature Machine Intelligence profile →
  2. Explicitly unbiased large language models still form biased associations
    2025 13 citations Xuechunzi Bai, Angelina Wang et al. Proceedings of the National Academy of Sciences profile →

Peers

Angelina Wang
Kimmo Kärkkäinen United States
Harsha Nori United States
Yuheng Li Australia
Ana Marasović United States
Samuel Jenkins United States
Tobias Huber Germany
Umang Bhatt United Kingdom
Kyle McDonell United Kingdom
Laria Reynolds United States
Robert H. Wortham United Kingdom
Kimmo Kärkkäinen United States
Angelina Wang
Citations per year, relative to Angelina Wang Angelina Wang (= 1×) peers Kimmo Kärkkäinen

Countries citing papers authored by Angelina Wang

Since Specialization
Citations

This map shows the geographic impact of Angelina Wang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Angelina Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Angelina Wang more than expected).

Fields of papers citing papers by Angelina Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Angelina Wang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Angelina Wang. The network helps show where Angelina Wang may publish in the future.

Co-authorship network of co-authors of Angelina Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Angelina Wang. A scholar is included among the top collaborators of Angelina Wang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Angelina Wang. Angelina Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Bai, Xuechunzi, Angelina Wang, Ilia Sucholutsky, & Thomas L. Griffiths. (2025). Explicitly unbiased large language models still form biased associations. Proceedings of the National Academy of Sciences. 122(8). e2416228122–e2416228122. 13 indexed citations breakdown →
2.
Wang, Angelina, Jamie Morgenstern, & John P. Dickerson. (2025). Large language models that replace human participants can harmfully misportray and flatten identity groups. Nature Machine Intelligence. 7(3). 400–411. 20 indexed citations breakdown →
3.
Wang, Angelina, Aaron Hertzmann, & Olga Russakovsky. (2024). Benchmark suites instead of leaderboards for evaluating AI fairness. Patterns. 5(11). 101080–101080. 3 indexed citations
4.
Wang, Angelina, et al.. (2024). Strategies for Increasing Corporate Responsible AI Prioritization. Proceedings of the AAAI/ACM Conference on AI Ethics and Society. 7. 1514–1526. 1 indexed citations
5.
Wang, Angelina, et al.. (2024). Visions of a Discipline: Analyzing Introductory AI Courses on YouTube. 2400–2420. 2 indexed citations
6.
Wang, Angelina, Sayash Kapoor, Solon Barocas, & Arvind Narayanan. (2023). Against Predictive Optimization. 626–626. 8 indexed citations
7.
Mathur, Arunesh, et al.. (2023). Manipulative tactics are the norm in political emails: Evidence from 300K emails from the 2020 US election cycle. Big Data & Society. 10(1). 10 indexed citations
8.
Wang, Angelina, Sayash Kapoor, Solon Barocas, & Arvind Narayanan. (2023). Against Predictive Optimization: On the Legitimacy of Decision-making Algorithms That Optimize Predictive Accuracy. 1(1). 1–45. 24 indexed citations
9.
Katzman, Jared, Angelina Wang, Morgan Klaus Scheuerman, et al.. (2023). Taxonomizing and Measuring Representational Harms: A Look at Image Tagging. Proceedings of the AAAI Conference on Artificial Intelligence. 37(12). 14277–14285. 12 indexed citations
10.
Wang, Angelina & Olga Russakovsky. (2023). Overwriting Pretrained Bias with Finetuning Data. 3934–3945. 7 indexed citations
11.
Wang, Angelina & Graham McPhail. (2023). Musical Futures in New Zealand: A study in recontextualisation. International Journal of Music Education. 42(1). 125–138. 5 indexed citations
12.
Wang, Angelina, et al.. (2023). Gender Artifacts in Visual Datasets. 4814–4825. 10 indexed citations
13.
Wang, Angelina, et al.. (2022). REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets. International Journal of Computer Vision. 130(7). 1790–1810. 39 indexed citations
14.
Wang, Angelina, V. Ramaswamy, & Olga Russakovsky. (2022). Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation. arXiv (Cornell University). 336–349. 50 indexed citations
15.
Wang, Angelina, et al.. (2022). Measuring Representational Harms in Image Captioning. 324–335. 24 indexed citations
16.
Wang, Angelina & Olga Russakovsky. (2021). Directional Bias Amplification. International Conference on Machine Learning. 10882–10893. 1 indexed citations
17.
Wang, Angelina, et al.. (2021). Understanding and Evaluating Racial Biases in Image Captioning. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 14810–14820. 46 indexed citations
18.
Wang, Angelina, et al.. (2019). Learning Robotic Manipulation through Visual Planning and Acting. 53 indexed citations
19.
Zhu, Shu, Yongjun Wang, Weiqiang Chen, et al.. (2016). High-Density Lipoprotein (HDL) Counter-Regulates Serum Amyloid A (SAA)-Induced sPLA2-IIE and sPLA2-V Expression in Macrophages. PLoS ONE. 11(11). e0167468–e0167468. 21 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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