Been Kim
Impact in
- Health Informatics top 0.5%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 1%
- Explainable Artificial Intelligence (XAI)
- Adversarial Robustness in Machine Learning
- Machine Learning and Data Classification
- Topic Modeling
- Machine Learning in Healthcare
- Anomaly Detection Techniques and Applications
Papers in
-
- Explainable Artificial Intelligence (XAI) 17
- Machine Learning and Data Classification 9
- Adversarial Robustness in Machine Learning 7
- AI-based Problem Solving and Planning 4
- Neural Networks and Applications 4
- Topic Modeling 3
- Speech and dialogue systems 2
- Co-authors
- Rajiv Khanna (2 shared papers)Oluwasanmi Koyejo (1 shared paper)Finale Doshi‐Velez (4 shared papers)Julie Shah (7 shared papers)Martin Wattenberg (6 shared papers)Emily Reif (4 shared papers)Justin Gilmer (3 shared papers)Julius Adebayo (2 shared papers)
- Journals
- Proceedings of the National Academy of Sciences (3 papers)AI Magazine (1 paper)Cell (1 paper)Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (1 paper)Data Mining and Knowledge Discovery (1 paper)
- Partner nations
- United StatesUnited KingdomSwitzerland
In The Last Decade
Been Kim
32 papers receiving 1.5k citations
Been Kim's Hit Papers
Peers
Comparison fields: 5 of 141
- Health Informatics 163
- Artificial Intelligence 1.1k
- Safety Research 120
- Computer Vision and Pattern Recognition 288
- Health Information Management 35
Countries citing papers authored by Been Kim
This map shows the geographic impact of Been Kim'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 Been Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Been Kim more than expected).
Fields of papers citing papers by Been Kim
This network shows the impact of papers produced by Been Kim. 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 Been Kim. The network helps show where Been Kim may publish in the future.
Co-authors
The 25 scholars most cited alongside Been Kim, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 33 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Examples are not enough, learn to criticize! Criticism for Interpretability Hit paper breakdown → | 2016 | 280 |
| 2 | 2019 | 242 | |
| 3 | Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments Hit paper breakdown → | 2021 | 210 |
| 4 | 2018 | 207 | |
| 5 | The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification | 2014 | 72 |
| 6 | Visualizing and Measuring the Geometry of BERT | 2019 | 70 |
| 7 | 2019 | 62 | |
| 8 | A Roadmap for a Rigorous Science of Interpretability. | 2017 | 56 |
| 9 | 2022 | 54 | |
| 10 | Mind the Gap: a generative approach to interpretable feature selection and extraction | 2015 | 53 |
| 11 | To Trust Or Not To Trust A Classifier | 2018 | 35 |
| 12 | 2024 | 31 | |
| 13 | 2021 | 27 | |
| 14 | iBCM: Interactive Bayesian Case Model Empowering Humans via Intuitive Interaction | 2015 | 18 |
| 15 | Do Neural Networks Show Gestalt Phenomena? An Exploration of the Law of Closure. | 2019 | 17 |
| 16 | 2021 | 13 | |
| 17 | TCAV: Relative concept importance testing with Linear Concept Activation Vectors | 2017 | 12 |
| 18 | 2018 | 11 | |
| 19 | 2023 | 10 | |
| 20 | BIM: Towards Quantitative Evaluation of Interpretability Methods with Ground Truth. | 2019 | 9 |
About Been Kim
Been Kim is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Cognitive Neuroscience and Signal Processing, having authored 33 papers that have together received 1.6k indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (17 papers), Machine Learning and Data Classification (9 papers), Adversarial Robustness in Machine Learning (7 papers), AI-based Problem Solving and Planning (4 papers), Neural Networks and Applications (4 papers), Topic Modeling (3 papers), Speech and dialogue systems (2 papers) and Sports Analytics and Performance (2 papers). The work is most often cited by research in Health Informatics (163 citations), Artificial Intelligence (1.1k citations), Safety Research (120 citations), Computer Vision and Pattern Recognition (288 citations) and Health Information Management (35 citations). Been Kim has collaborated with scholars based in United States, United Kingdom and Switzerland. Frequent co-authors include Rajiv Khanna, Oluwasanmi Koyejo, Finale Doshi‐Velez, Julie Shah, Martin Wattenberg, Emily Reif, Justin Gilmer, Julius Adebayo, Ian Goodfellow and Michael Muelly. Their work appears in journals such as Proceedings of the National Academy of Sciences, AI Magazine, Cell, Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences and Data Mining and Knowledge Discovery.
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.