Dylan Slack
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 5%
- Explainable Artificial Intelligence (XAI)
- Adversarial Robustness in Machine Learning
- Machine Learning in Healthcare
- Machine Learning and Data Classification
- Anomaly Detection Techniques and Applications
- Topic Modeling
Papers in
-
- Explainable Artificial Intelligence (XAI) 4
- Adversarial Robustness in Machine Learning 3
- Topic Modeling 2
- Machine Learning and Data Classification 1
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- Artificial Intelligence in Healthcare and Education 2
- Co-authors
- Sameer Singh (3 shared papers)Himabindu Lakkaraju (3 shared papers)Emily Jia (2 shared papers)Sophie Hilgard (2 shared papers)Sorelle A. Friedler (2 shared papers)Vincent F. Yu (1 shared paper)Joshua Schrier (1 shared paper)Alexander J. Norquist (1 shared paper)
- Journals
- The Journal of Chemical Physics (1 paper)Nature Machine Intelligence (1 paper)arXiv (Cornell University) (1 paper)Proceedings of the AAAI/ACM Conference on AI Ethics and Society (1 paper)
- Partner nations
- United States
In The Last Decade
Dylan Slack
6 papers receiving 550 citations
Dylan Slack's Hit Papers
Peers
Comparison fields: 5 of 103
- Health Informatics 60
- Artificial Intelligence 388
- Safety Research 68
- Information Systems and Management 36
- Health Information Management 10
Countries citing papers authored by Dylan Slack
This map shows the geographic impact of Dylan Slack'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 Dylan Slack with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dylan Slack more than expected).
Fields of papers citing papers by Dylan Slack
This network shows the impact of papers produced by Dylan Slack. 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 Dylan Slack. The network helps show where Dylan Slack may publish in the future.
Co-authors
The 20 scholars most cited alongside Dylan Slack, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Fooling LIME and SHAP Hit paper breakdown → | 2020 | 455 |
| 2 | 2023 | 58 | |
| 3 | 2020 | 22 | |
| 4 | 2022 | 17 | |
| 5 | How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods. | 2019 | 11 |
| 6 | 2021 | 3 |
About Dylan Slack
Dylan Slack is a scholar working on Artificial Intelligence, Health Informatics, Safety Research, Management Science and Operations Research and Materials Chemistry, having authored 6 papers that have together received 566 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (4 papers), Adversarial Robustness in Machine Learning (3 papers), Artificial Intelligence in Healthcare and Education (2 papers), Topic Modeling (2 papers), Machine Learning and Data Classification (1 paper), Stock Market Forecasting Methods (1 paper), Ethics and Social Impacts of AI (1 paper) and Machine Learning in Materials Science (1 paper). The work is most often cited by research in Health Informatics (60 citations), Artificial Intelligence (388 citations), Safety Research (68 citations), Information Systems and Management (36 citations) and Health Information Management (10 citations). Dylan Slack has collaborated with scholars based in United States. Frequent co-authors include Sameer Singh, Himabindu Lakkaraju, Emily Jia, Sophie Hilgard, Sorelle A. Friedler, Vincent F. Yu, Joshua Schrier, Alexander J. Norquist, Xiaorong Wang and Emory M. Chan. Their work appears in journals such as The Journal of Chemical Physics, Nature Machine Intelligence, arXiv (Cornell University) and Proceedings of the AAAI/ACM Conference on AI Ethics and Society.
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.