Harini Suresh

2.3k total citations · 1 hit paper
18 papers, 616 citations indexed

About

Harini Suresh is a scholar working on Health Informatics, Artificial Intelligence and Safety Research. According to data from OpenAlex, Harini Suresh has authored 18 papers receiving a total of 616 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Health Informatics, 7 papers in Artificial Intelligence and 5 papers in Safety Research. Recurrent topics in Harini Suresh's work include Artificial Intelligence in Healthcare and Education (7 papers), Ethics and Social Impacts of AI (5 papers) and Explainable Artificial Intelligence (XAI) (4 papers). Harini Suresh is often cited by papers focused on Artificial Intelligence in Healthcare and Education (7 papers), Ethics and Social Impacts of AI (5 papers) and Explainable Artificial Intelligence (XAI) (4 papers). Harini Suresh collaborates with scholars based in United States, Germany and Canada. Harini Suresh's co-authors include John V. Guttag, Marzyeh Ghassemi, Eva Lermer, Errol Colak, Joseph F. Coughlin, Martina Raue, Susanne Gaube, Seth J. Berkowitz, Alexander Merritt and Catherine D’Ignazio and has published in prestigious journals such as Advanced Materials, SHILAP Revista de lepidopterología and Scientific Reports.

In The Last Decade

Harini Suresh

17 papers receiving 594 citations

Hit Papers

align trajectories

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.

Do as AI say: susceptibility in deployment of clinical decision-aids

2021 243 citations Susanne Gaube, Harini Suresh et al. npj Digital Medicine profile →

Peers

Harini Suresh

AI

HI

SR

RNMI

SPS

Caio C. Vieira Machado United Kingdom

Mohammad Hosseini United States

Martina Raue Germany

Francesco Baglivo Italy

Thilo Hagendorff Germany

Christopher Burr United Kingdom

Sonia Allan Australia

Boris Babic United States

Lixiang Yan Australia

Damian Eke United Kingdom

Caio C. Vieira Machado United Kingdom

Harini Suresh

145 ×0.6

AI

317 ×1.4

HI

131 ×0.9

SR

93 ×1.0

RNMI

73 ×1.2

SPS

Citations per year, relative to Harini Suresh Harini Suresh (= 1×) peers Caio C. Vieira Machado

Countries citing papers authored by Harini Suresh

Since Specialization

Citations

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

Fields of papers citing papers by Harini Suresh

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Harini Suresh. 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 Harini Suresh. The network helps show where Harini Suresh may publish in the future.

Co-authorship network of co-authors of Harini Suresh

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

All Works

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18 of 18 papers shown

1.

Kuo, Tzu-Sheng, Amy Winecoff, Emily Tseng, et al.. (2025). Sociotechnical AI Governance: Challenges and Opportunities for HCI. 1–6. 1 indexed citations

2.

Pierson, Emma, Jon Kleinberg, Mark Dredze, et al.. (2025). Using Large Language Models to Promote Health Equity. NEJM AI. 2(2). 6 indexed citations

3.

Iftikhar, Zainab, et al.. (2025). How LLM Counselors Violate Ethical Standards in Mental Health Practice: A Practitioner-Informed Framework. Proceedings of the AAAI/ACM Conference on AI Ethics and Society. 8(2). 1311–1323. 1 indexed citations

4.

Tseng, Emily, et al.. (2025). "Ownership, Not Just Happy Talk": Co-Designing a Participatory Large Language Model for Journalism. Radboud Repository (Radboud University). 3119–3130.

5.

Hasan, Alifia, Katherine C. Kellogg, William Ratliff, et al.. (2024). Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities. SHILAP Revista de lepidopterología. 3(5). e0000390–e0000390. 22 indexed citations

6.

Suresh, Harini, et al.. (2023). Kaleidoscope: Semantically-grounded, context-specific ML model evaluation. 1–13. 2 indexed citations

7.

Suresh, Harini, et al.. (2023). Saliency Cards: A Framework to Characterize and Compare Saliency Methods. 285–296. 3 indexed citations

8.

Gaube, Susanne, Harini Suresh, Martina Raue, et al.. (2023). Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays. Scientific Reports. 13(1). 1383–1383. 43 indexed citations

9.

D’Ignazio, Catherine, et al.. (2022). Feminicide and counterdata production: Activist efforts to monitor and challenge gender-related violence. Patterns. 3(7). 100530–100530. 19 indexed citations

10.

Suresh, Harini, et al.. (2022). Towards Intersectional Feminist and Participatory ML: A Case Study in Supporting Feminicide Counterdata Collection. Queen Mary Research Online (Queen Mary University of London). 667–678. 36 indexed citations

11.

Boag, William, et al.. (2022). Tech Worker Organizing for Power and Accountability. 452–463. 17 indexed citations

12.

Gaube, Susanne, Harini Suresh, Martina Raue, et al.. (2021). Do as AI say: susceptibility in deployment of clinical decision-aids. npj Digital Medicine. 4(1). 31–31. 243 indexed citations breakdown →

13.

Suresh, Harini & John V. Guttag. (2021). Understanding Potential Sources of Harm throughout the Machine Learning Life Cycle. 16 indexed citations

14.

Suresh, Harini & John V. Guttag. (2019). A Framework for Understanding Unintended Consequences of Machine Learning. 126 indexed citations

15.

Boag, Willie, Harini Suresh, Leo Anthony Celi, Peter Szolovits, & Marzyeh Ghassemi. (2018). Racial Disparities and Mistrust in End-of-Life Care. arXiv (Cornell University). 587–602. 1 indexed citations

16.

Lacson, Ronilda, Bowen Baker, Harini Suresh, et al.. (2018). Use of machine-learning algorithms to determine features of systolic blood pressure variability that predict poor outcomes in hypertensive patients. Clinical Kidney Journal. 12(2). 206–212. 31 indexed citations

17.

McDermott, Matthew B. A., Tristan Naumann, Nathan Hunt, et al.. (2018). Semi-Supervised Biomedical Translation With Cycle Wasserstein Regression GANs. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 13 indexed citations

18.

Gilbert, Jonathan B., et al.. (2013). Orientation‐Specific Attachment of Polymeric Microtubes on Cell Surfaces. Advanced Materials. 25(41). 5948–5952. 36 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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