World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
13613
World Ranking
8167
National Ranking
3501

Been Kim publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Been Kim sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 62 publications — 1st percentile

1% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Been Kim D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Been Kim sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 42 D-Index — 43rd percentile

43% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Been Kim is a researcher affiliated with Google in the United States, specializing in computer science with a focus on artificial intelligence and related fields. Their work spans various subfields including artificial intelligence, computer vision and pattern recognition, cognitive neuroscience, economics and econometrics, and materials chemistry. The predominant area of research is artificial intelligence, supported by a substantial volume of publications in this domain.

The main topics addressed by Been Kim include:

  • Explainable Artificial Intelligence (XAI)
  • Adversarial Robustness in Machine Learning
  • Sports Analytics and Performance
  • Machine Learning in Healthcare
  • Artificial Intelligence in Games
  • Machine Learning in Materials Science
  • Bayesian Modeling and Causal Inference

Been Kim has contributed to a variety of publication venues, with a strong presence on arXiv, where most of their work is disseminated. Other frequent venues include the Proceedings of the National Academy of Sciences and Pattern Recognition. The count of publications in some key venues includes arXiv (21), Proceedings of the National Academy of Sciences (3), and Pattern Recognition (1).

Key recent papers by Been Kim include:

  • "Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments" (2021) in Pattern Recognition
  • "Just Say No to Single Embeddings: Why Your AI Needs Multiple Perspectives" (2025) in arXiv (Cornell University)
  • "Concept Bottleneck Models" (2020) in arXiv (Cornell University)
  • "Acquisition of chess knowledge in AlphaZero" (2022) in Proceedings of the National Academy of Sciences
  • "Impossibility theorems for feature attribution" (2024) in Proceedings of the National Academy of Sciences

Been Kim collaborates frequently with several researchers, including Nenad Tomašev, Demis Hassabis, Ulrich Paquet, Thomas McGrath, and Andrei Kapishnikov, each with multiple joint publications. These collaborations contribute to a diverse and interdisciplinary research output.

Best Publications

  • Towards A Rigorous Science of Interpretable Machine Learning

    Finale Doshi-Velez;Been Kim

  • SmoothGrad: removing noise by adding noise

    Daniel Smilkov;Nikhil Thorat;Been Kim;Fernanda B. Viégas

  • Sanity Checks for Saliency Maps

    Julius Adebayo;Justin Gilmer;Michael Christoph Muelly;Ian Goodfellow

  • Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

    Been Kim;Martin Wattenberg;Justin Gilmer;Carrie Jun Cai

  • Examples are not enough, learn to criticize! Criticism for Interpretability

    Been Kim;Rajiv Khanna;Oluwasanmi O. Koyejo

  • The (Un)reliability of saliency methods

    Pieter-Jan Kindermans;Sara Hooker;Julius Adebayo;Maximilian Alber

  • A Benchmark for Interpretability Methods in Deep Neural Networks

    Sara Hooker;Dumitru Erhan;Pieter-Jan Kindermans;Been Kim

  • Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

    Been Kim;Martin Wattenberg;Justin Gilmer;Carrie Cai

  • Towards Automatic Concept-based Explanations

    Amirata Ghorbani;James Wexler;James Y. Zou;Been Kim

  • Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making

    Carrie J. Cai;Emily Reif;Narayan Hegde;Jason Hipp

  • To Trust Or Not To Trust A Classifier

    Heinrich Jiang;Been Kim;Melody Y. Guan;Maya R. Gupta

  • Learning how to explain neural networks: PatternNet and PatternAttribution

    Pieter Jan Kindermans;Kristof T. Schütt;Maximilian Alber;Klaus Robert Müller

  • The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification

    Been Kim;Cynthia Rudin;Julie A Shah

  • Multiple relative pose graphs for robust cooperative mapping

    Been Kim;Michael Kaess;Luke Fletcher;John Leonard

  • Visualizing and Measuring the Geometry of BERT

    Emily Reif;Ann Yuan;Martin Wattenberg;Fernanda B. Viegas

  • Considerations for Evaluation and Generalization in Interpretable Machine Learning

    Finale Doshi-Velez;Been Kim

  • Concept Bottleneck Models

    Pang Wei Koh;Thao Nguyen;Yew Siang Tang;Stephen Mussmann

  • An Evaluation of the Human-Interpretability of Explanation

    Isaac Lage;Emily Chen;Jeffrey He;Menaka Narayanan

  • Explaining Classifiers with Causal Concept Effect (CaCE).

    Yash Goyal;Amir Feder;Uri Shalit;Been Kim

  • How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation.

    Menaka Narayanan;Emily Chen;Jeffrey He;Been Kim

  • A Roadmap for a Rigorous Science of Interpretability.

    Finale Doshi-Velez;Been Kim

  • Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems

    Shalmali Joshi;Oluwasanmi Koyejo;Warut Vijitbenjaronk;Been Kim

  • Visualizing and Measuring the Geometry of BERT

    Andy Coenen;Emily Reif;Ann Yuan;Been Kim

Frequent Co-Authors

Martin Wattenberg
Martin Wattenberg Harvard University
Finale Doshi-Velez
Finale Doshi-Velez Harvard University
Fernanda B. Viégas
Fernanda B. Viégas Harvard University
Dumitru Erhan
Dumitru Erhan Google (United States)
Cynthia Rudin
Cynthia Rudin Duke University
Joydeep Ghosh
Joydeep Ghosh The University of Texas at Austin
Ian Goodfellow
Ian Goodfellow Google (United States)
Samy Bengio
Samy Bengio Apple (United States)
Samuel J. Gershman
Samuel J. Gershman Harvard University

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