Michelle S. Lam

462 total citations
12 papers, 216 citations indexed

About

Michelle S. Lam is a scholar working on Artificial Intelligence, Safety Research and Computer Science Applications. According to data from OpenAlex, Michelle S. Lam has authored 12 papers receiving a total of 216 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 6 papers in Safety Research and 4 papers in Computer Science Applications. Recurrent topics in Michelle S. Lam's work include Ethics and Social Impacts of AI (6 papers), Explainable Artificial Intelligence (XAI) (3 papers) and Hate Speech and Cyberbullying Detection (3 papers). Michelle S. Lam is often cited by papers focused on Ethics and Social Impacts of AI (6 papers), Explainable Artificial Intelligence (XAI) (3 papers) and Hate Speech and Cyberbullying Detection (3 papers). Michelle S. Lam collaborates with scholars based in United States, South Korea and Mexico. Michelle S. Lam's co-authors include Michael S. Bernstein, Jeffrey T. Hancock, Mitchell Gordon, Tatsunori Hashimoto, Kayur Patel, Joon Sung Park, Ryo Suzuki, Niloufar Salehi, Danaë Metaxa and James A. Landay and has published in prestigious journals such as ACM Transactions on Computer-Human Interaction, Proceedings of the ACM on Human-Computer Interaction and CHI Conference on Human Factors in Computing Systems.

In The Last Decade

Michelle S. Lam

12 papers receiving 208 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Michelle S. Lam United States	7	113	56	54	52	32	12	216
Jonathan Stray United States	6	78 0.7×	18 0.3×	49 0.9×	88 1.7×	50 1.6×	11	251
Tianling Yang Germany	5	80 0.7×	51 0.9×	133 2.5×	69 1.3×	12 0.4×	8	265
Samuel Carton United States	11	225 2.0×	22 0.4×	61 1.1×	64 1.2×	61 1.9×	15	351
Jack Bandy United States	7	71 0.6×	17 0.3×	62 1.1×	110 2.1×	67 2.1×	15	238
Riadh Jeljeli United Arab Emirates	8	100 0.9×	48 0.9×	21 0.4×	33 0.6×	12 0.4×	22	227
Claudia Müller-Birn Germany	9	79 0.7×	42 0.8×	33 0.6×	35 0.7×	70 2.2×	45	231
David Gray Widder United States	9	76 0.7×	41 0.7×	125 2.3×	41 0.8×	8 0.3×	21	256
Esin Durmus United States	10	280 2.5×	11 0.2×	40 0.7×	45 0.9×	20 0.6×	18	432
Julia Bullard United States	8	39 0.3×	38 0.7×	22 0.4×	67 1.3×	25 0.8×	31	267
Quan Ze Chen United States	7	374 3.3×	25 0.4×	15 0.3×	47 0.9×	34 1.1×	11	464

Countries citing papers authored by Michelle S. Lam

Since Specialization

Citations

This map shows the geographic impact of Michelle S. Lam'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 Michelle S. Lam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michelle S. Lam more than expected).

Fields of papers citing papers by Michelle S. Lam

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michelle S. Lam. 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 Michelle S. Lam. The network helps show where Michelle S. Lam may publish in the future.

Co-authorship network of co-authors of Michelle S. Lam

This figure shows the co-authorship network connecting the top 25 collaborators of Michelle S. Lam. A scholar is included among the top collaborators of Michelle S. Lam 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 Michelle S. Lam. Michelle S. Lam is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

12 of 12 papers shown

1.

Lam, Michelle S., et al.. (2025). Designing LLM Chains by Adapting Techniques from Crowdsourcing Workflows. ACM Transactions on Computer-Human Interaction. 32(3). 1–57. 3 indexed citations

2.

Lam, Michelle S., Motahhare Eslami, Juho Kim, et al.. (2025). Human-Centered Evaluation and Auditing of Language Models. 1–7. 1 indexed citations

3.

Lam, Michelle S.. (2024). Granting Non-AI Experts Creative Control Over AI Systems. 1–5. 1 indexed citations

4.

Xiao, Ziang, Michelle S. Lam, Motahhare Eslami, et al.. (2024). Human-Centered Evaluation and Auditing of Language Models. 1–6. 11 indexed citations

5.

Jia, Chenyan, et al.. (2024). Embedding Democratic Values into Social Media AIs via Societal Objective Functions. Proceedings of the ACM on Human-Computer Interaction. 8(CSCW1). 1–36. 12 indexed citations

6.

Lam, Michelle S., et al.. (2023). Supporting User Engagement in Testing, Auditing, and Contesting AI. 556–559. 6 indexed citations

7.

Bernstein, Michael S., Angèle Christin, Jeffrey T. Hancock, et al.. (2023). Embedding Societal Values into Social Media Algorithms. 2(1). 11 indexed citations

8.

Lam, Michelle S., et al.. (2023). Sociotechnical Audits: Broadening the Algorithm Auditing Lens to Investigate Targeted Advertising. Proceedings of the ACM on Human-Computer Interaction. 7(CSCW2). 1–37. 16 indexed citations

9.

Lam, Michelle S., Mitchell Gordon, Danaë Metaxa, et al.. (2022). End-User Audits: A System Empowering Communities to Lead Large-Scale Investigations of Harmful Algorithmic Behavior. Proceedings of the ACM on Human-Computer Interaction. 6(CSCW2). 1–34. 25 indexed citations

10.

Gordon, Mitchell, Michelle S. Lam, Joon Sung Park, et al.. (2022). Jury Learning: Integrating Dissenting Voices into Machine Learning Models. CHI Conference on Human Factors in Computing Systems. 1–19. 81 indexed citations

11.

Lam, Michelle S., et al.. (2019). Eevee. 1–6. 1 indexed citations

12.

Suzuki, Ryo, et al.. (2016). Atelier. 2645–2656. 48 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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