Finale Doshi‐Velez
Impact in
- Health Informatics top 0.05%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 0.5%
- Machine Learning in Healthcare
- Explainable Artificial Intelligence (XAI)
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
- Topic Modeling
Papers in
-
- Machine Learning in Healthcare 19
- Explainable Artificial Intelligence (XAI) 18
- Topic Modeling 13
- Reinforcement Learning in Robotics 12
- Adversarial Robustness in Machine Learning 11
- Bayesian Methods and Mixture Models 10
- Machine Learning and Algorithms 9
-
- Sepsis Diagnosis and Treatment 9
- Co-authors
- Andrew Slavin Ross (5 shared papers)Isaac S. Kohane (3 shared papers)Yaorong Ge (2 shared papers)Leo Anthony Celi (5 shared papers)Marzyeh Ghassemi (5 shared papers)Been Kim (4 shared papers)Joseph Futoma (3 shared papers)Morgan Simons (2 shared papers)
- Journals
- Journal of Affective Disorders (3 papers)JAMA Network Open (2 papers)Nature Medicine (2 papers)PEDIATRICS (2 papers)Translational Psychiatry (2 papers)
- Partner nations
- United StatesUnited KingdomSwitzerland
In The Last Decade
Finale Doshi‐Velez
97 papers receiving 3.3k citations
Finale Doshi‐Velez's Hit Papers
Peers
Comparison fields: 5 of 172
- Health Informatics 667
- Artificial Intelligence 1.9k
- Health Information Management 244
- Family Practice 63
- Safety Research 153
Countries citing papers authored by Finale Doshi‐Velez
This map shows the geographic impact of Finale Doshi‐Velez'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 Finale Doshi‐Velez with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Finale Doshi‐Velez more than expected).
Fields of papers citing papers by Finale Doshi‐Velez
This network shows the impact of papers produced by Finale Doshi‐Velez. 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 Finale Doshi‐Velez. The network helps show where Finale Doshi‐Velez may publish in the future.
Co-authors
The 25 scholars most cited alongside Finale Doshi‐Velez, 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 102 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Do no harm: a roadmap for responsible machine learning for health care Hit paper breakdown → | 2019 | 506 |
| 2 | Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients Hit paper breakdown → | 2018 | 282 |
| 3 | 2013 | 282 | |
| 4 | The myth of generalisability in clinical research and machine learning in health care Hit paper breakdown → | 2020 | 252 |
| 5 | 2018 | 177 | |
| 6 | 2021 | 137 | |
| 7 | 2018 | 136 | |
| 8 | 2014 | 114 | |
| 9 | Ethical and regulatory challenges of large language models in medicine Hit paper breakdown → | 2024 | 105 |
| 10 | 2017 | 98 | |
| 11 | 2011 | 93 | |
| 12 | A Bayesian framework for learning rule sets for interpretable classification | 2017 | 90 |
| 13 | 2019 | 59 | |
| 14 | A Roadmap for a Rigorous Science of Interpretability. | 2017 | 55 |
| 15 | Mind the Gap: a generative approach to interpretable feature selection and extraction | 2015 | 53 |
| 16 | The Infinite Partially Observable Markov Decision Process | 2009 | 44 |
| 17 | 2017 | 42 | |
| 18 | 2015 | 41 | |
| 19 | 2023 | 39 | |
| 20 | 2017 | 37 |
About Finale Doshi‐Velez
Finale Doshi‐Velez is a scholar working on Artificial Intelligence, Epidemiology, Pharmacology, Health Informatics and Experimental and Cognitive Psychology, having authored 102 papers that have together received 3.4k indexed citations. Recurring topics across this work include Machine Learning in Healthcare (19 papers), Explainable Artificial Intelligence (XAI) (18 papers), Topic Modeling (13 papers), Reinforcement Learning in Robotics (12 papers), Adversarial Robustness in Machine Learning (11 papers), Bayesian Methods and Mixture Models (10 papers), Machine Learning and Algorithms (9 papers) and Sepsis Diagnosis and Treatment (9 papers). The work is most often cited by research in Health Informatics (667 citations), Artificial Intelligence (1.9k citations), Health Information Management (244 citations), Family Practice (63 citations) and Safety Research (153 citations). Finale Doshi‐Velez has collaborated with scholars based in United States, United Kingdom and Switzerland. Frequent co-authors include Andrew Slavin Ross, Isaac S. Kohane, Yaorong Ge, Leo Anthony Celi, Marzyeh Ghassemi, Been Kim, Joseph Futoma, Morgan Simons, Trishan Panch and Mohammed Saeed. Their work appears in journals such as Journal of Affective Disorders, JAMA Network Open, Nature Medicine, PEDIATRICS and Translational Psychiatry.
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.