Manish Raghavan

3.2k total citations · 1 hit paper
28 papers, 632 citations indexed

About

Manish Raghavan is a scholar working on Artificial Intelligence, Safety Research and Management Science and Operations Research. According to data from OpenAlex, Manish Raghavan has authored 28 papers receiving a total of 632 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 10 papers in Safety Research and 9 papers in Management Science and Operations Research. Recurrent topics in Manish Raghavan's work include Ethics and Social Impacts of AI (9 papers), Explainable Artificial Intelligence (XAI) (7 papers) and Decision-Making and Behavioral Economics (3 papers). Manish Raghavan is often cited by papers focused on Ethics and Social Impacts of AI (9 papers), Explainable Artificial Intelligence (XAI) (7 papers) and Decision-Making and Behavioral Economics (3 papers). Manish Raghavan collaborates with scholars based in United States, United Kingdom and Canada. Manish Raghavan's co-authors include Jon Kleinberg, Sendhil Mullainathan, Solon Barocas, Karen Levy, Geoff Pleiss, Kilian Q. Weinberger, Felix Wu, Nilesh Dalvi, Philip Bohannon and J. Ludwig and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Management Science and Communications of the ACM.

In The Last Decade

Manish Raghavan

26 papers receiving 602 citations

Hit Papers

Inherent Trade-Offs in the Fair Determination of Risk Scores 2017 2026 2020 2023 2017 50 100 150 200 250
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. Inherent Trade-Offs in the Fair Determination of Risk Scores
    2017 262 citations Jon Kleinberg, Sendhil Mullainathan et al. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Manish Raghavan United States 9 345 306 105 72 69 28 632
Hoda Heidari United States 9 491 1.4× 372 1.2× 182 1.7× 106 1.5× 74 1.1× 41 868
Sahil Verma India 5 366 1.1× 364 1.2× 88 0.8× 58 0.8× 72 1.0× 5 636
Nina Grgić-Hlača Germany 10 308 0.9× 208 0.7× 91 0.9× 72 1.0× 53 0.8× 15 447
Jason W. Burton United Kingdom 4 198 0.6× 142 0.5× 120 1.1× 68 0.9× 30 0.4× 9 463
Marija Slavkovik Norway 10 193 0.6× 178 0.6× 73 0.7× 90 1.3× 43 0.6× 44 408
Muhammad Bilal Zafar Germany 13 351 1.0× 466 1.5× 190 1.8× 41 0.6× 214 3.1× 23 878
Jakob Mökander United Kingdom 11 326 0.9× 271 0.9× 63 0.6× 79 1.1× 95 1.4× 27 582
Pranay Lohia India 6 358 1.0× 354 1.2× 58 0.6× 42 0.6× 84 1.2× 13 650
Emmanuel Letouzé United States 8 237 0.7× 167 0.5× 184 1.8× 41 0.6× 91 1.3× 13 583
Amit Datta United States 10 156 0.5× 190 0.6× 157 1.5× 26 0.4× 91 1.3× 28 588

Countries citing papers authored by Manish Raghavan

Since Specialization
Citations

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

Fields of papers citing papers by Manish Raghavan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Manish Raghavan

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

All Works

20 of 20 papers shown
1.
Raghavan, Manish, et al.. (2025). What Constitutes a Less Discriminatory Algorithm?. 136–151.
2.
Raghavan, Manish, et al.. (2024). Reconciling the Accuracy-Diversity Trade-off in Recommendations. 1318–1329. 4 indexed citations
3.
Raghavan, Manish, et al.. (2024). The Impact of Generative AI on Labor Market Matching. 1 indexed citations
4.
Raghavan, Manish, et al.. (2023). The Right to Be an Exception to a Data-Driven Rule. 2 indexed citations
5.
Morewedge, Carey K., Sendhil Mullainathan, Cass R. Sunstein, et al.. (2023). Human bias in algorithm design. Nature Human Behaviour. 7(11). 1822–1824. 20 indexed citations
6.
Raghavan, Manish. (2023). What Should We Do when Our Ideas of Fairness Conflict?. Communications of the ACM. 67(1). 88–97. 3 indexed citations
7.
8.
Kleinberg, Jon, Jens Ludwig, Sendhil Mullainathan, & Manish Raghavan. (2023). The Inversion Problem: Why Algorithms Should Infer Mental State and Not Just Predict Behavior. Perspectives on Psychological Science. 19(5). 827–838. 7 indexed citations
9.
Raghavan, Manish. (2023). The Societal Impacts of Algorithmic Decision-Making. 2 indexed citations
10.
Raghavan, Manish, et al.. (2022). Model Multiplicity: Opportunities, Concerns, and Solutions. 850–863. 33 indexed citations
11.
Danks, David, Craig R. Fox, Kristian J. Hammond, et al.. (2022). Hybrid Intelligence: A Paradigm for More Responsible Practice. SSRN Electronic Journal. 6 indexed citations
12.
Raghavan, Manish, et al.. (2021). Real Time Rainfall Prediction for Hyderabad Region using Machine learning Approach. 2021 Innovations in Power and Advanced Computing Technologies (i-PACT). 1–6. 3 indexed citations
13.
Kleinberg, Jon & Manish Raghavan. (2021). Algorithmic monoculture and social welfare. Proceedings of the National Academy of Sciences. 118(22). 44 indexed citations
14.
Kleinberg, Jon & Manish Raghavan. (2020). Algorithmic classification and strategic effort. ACM SIGecom Exchanges. 18(2). 45–52. 1 indexed citations
15.
Raghavan, Manish, Solon Barocas, Jon Kleinberg, & Karen Levy. (2019). Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices. arXiv (Cornell University). 75 indexed citations
16.
Raghavan, Manish, Solon Barocas, Jon Kleinberg, & Karen Levy. (2019). Mitigating Bias in Algorithmic Employment Screening: Evaluating Claims and Practices. SSRN Electronic Journal. 2 indexed citations
17.
Kleinberg, Jon & Manish Raghavan. (2018). Selection Problems in the Presence of Implicit Bias. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics). 94. 17. 15 indexed citations
18.
Kleinberg, Jon, Sendhil Mullainathan, & Manish Raghavan. (2017). Inherent Trade-Offs in the Fair Determination of Risk Scores. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics). 262 indexed citations breakdown →
19.
Kleinberg, Jon, Sigal Oren, & Manish Raghavan. (2017). Planning with Multiple Biases. 567–584. 6 indexed citations
20.
Raghavan, Manish. (1999). Some Aspects of Growth and Distribution of Rice in India. Social Scientist. 27(5/6). 62–62. 1 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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