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
- Neural Networks and Applications 4
- AI-based Problem Solving and Planning 4
- Topic Modeling 3
-
- Multimodal Machine Learning Applications 2
- Visual Attention and Saliency Detection 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)Ian Goodfellow (2 shared papers)
- Journals
- Proceedings of the National Academy of Sciences (3 papers)Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (1 paper)Data Mining and Knowledge Discovery (1 paper)Journal of Artificial Intelligence Research (1 paper)Cell (1 paper)
- Partner nations
- United StatesUnited KingdomSwitzerland
In The Last Decade
Been Kim
31 papers receiving 1.4k citations
Hit Papers
Peers
Comparison fields: 5 of 141
- Health Informatics 164
- Artificial Intelligence 1.1k
- Safety Research 117
- Computer Vision and Pattern Recognition 280
- Health Information Management 37
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 32 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 | 277 |
| 2 | 2019 | 222 | |
| 3 | Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments Hit paper breakdown → | 2021 | 208 |
| 4 | 2018 | 206 | |
| 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 | 59 | |
| 8 | A Roadmap for a Rigorous Science of Interpretability. | 2017 | 55 |
| 9 | Mind the Gap: a generative approach to interpretable feature selection and extraction | 2015 | 53 |
| 10 | 2022 | 51 | |
| 11 | To Trust Or Not To Trust A Classifier | 2018 | 35 |
| 12 | 2021 | 27 | |
| 13 | 2024 | 24 | |
| 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 | BIM: Towards Quantitative Evaluation of Interpretability Methods with Ground Truth. | 2019 | 9 |
| 20 | Interpreting black box predictions using fisher kernels | 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 32 papers that have together received 1.5k 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), Neural Networks and Applications (4 papers), AI-based Problem Solving and Planning (4 papers), Topic Modeling (3 papers), Multimodal Machine Learning Applications (2 papers) and Visual Attention and Saliency Detection (2 papers). The work is most often cited by research in Health Informatics (164 citations), Artificial Intelligence (1.1k citations), Safety Research (117 citations), Computer Vision and Pattern Recognition (280 citations) and Health Information Management (37 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, Ian Goodfellow, Julius Adebayo and Michael Muelly. Their work appears in journals such as Proceedings of the National Academy of Sciences, Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, Data Mining and Knowledge Discovery, Journal of Artificial Intelligence Research and Cell.
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.