Manish Raghavan

26 papers receiving 602 citations

Manish Raghavan's Hit Papers

Inherent Trade-Offs in the Fair Determination of Risk Scores 2017 · 262 citations
2620+3+6Years since publication50100150200250

Peers

Manish Raghavan
Comparison fields: 5 of 90
  • Health Informatics 57
  • Safety Research 345
  • Artificial Intelligence 306
  • General Decision Sciences 12
  • Management Science and Operations Research 64
Replace Hoda Heidari with:
Hoda Heidari United States
Nina Grgić-Hlača Germany
Muhammad Bilal Zafar Germany
Marija Slavkovik Norway
Emmanuel Letouzé United States
Jason W. Burton United Kingdom
Hasan Mahmud Bangladesh
Ekaterina Jussupow Germany
Amit Datta United States
Sahil Verma India
Manish Raghavan relative to Hoda Heidari United States Hoda Heidari's profile →
Citations per field
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Citations per year

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-authors

The 25 scholars most cited alongside Manish Raghavan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Manish Raghavan Line = papers co-authored together Manish Raghavan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Inherent Trade-Offs in the Fair Determination of Risk Scores
Hit paper breakdown →
2017262
2 201791
3 201975
4 202144
5 202233
6 202320
7 201418
8 201815
9 202314
10 20238
11 20237
12 20227
13 20226
14 20176
15 20244
16 20233
17 20213
18 20233
19 20232
20 20192

About Manish Raghavan

Manish Raghavan is a scholar working on Artificial Intelligence, Safety Research, Management Science and Operations Research, Sociology and Political Science and Information Systems, having authored 28 papers that have together received 632 indexed citations. Recurring topics across this work include Ethics and Social Impacts of AI (9 papers), Explainable Artificial Intelligence (XAI) (7 papers), Decision-Making and Behavioral Economics (3 papers), Digital Economy and Work Transformation (3 papers), Spam and Phishing Detection (2 papers), Hydrological Forecasting Using AI (2 papers), Auction Theory and Applications (2 papers) and Consumer Market Behavior and Pricing (2 papers). The work is most often cited by research in Health Informatics (57 citations), Safety Research (345 citations), Artificial Intelligence (306 citations), General Decision Sciences (12 citations) and Management Science and Operations Research (64 citations). Manish Raghavan has collaborated with scholars based in United States, United Kingdom and Canada. Frequent 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. Their work appears in journals such as Communications of the ACM, Perspectives on Psychological Science, Management Science, SIAM Journal on Computing and Nature Human Behaviour.

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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