Tom Stepleton
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
- Safety Research top 5%
- Ethics and Social Impacts of AI
Papers in
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- Ethics and Social Impacts of AI 4
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- Privacy-Preserving Technologies in Data 1
- Adversarial Robustness in Machine Learning 1
- Hate Speech and Cyberbullying Detection 1
- Reinforcement Learning in Robotics 1
- Co-authors
- Heinrich Jiang (2 shared papers)Aldo Pacchiano (2 shared papers)Silvia Chiappa (2 shared papers)John Aslanides (1 shared paper)Jason M. Samonds (1 shared paper)Nahema Marchal (1 shared paper)Lisa Anne Hendricks (1 shared paper)Arianna Manzini (1 shared paper)
- Journals
- Lecture notes in computer science (1 paper)Uncertainty in Artificial Intelligence (1 paper)Cambridge University Press eBooks (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)Proceedings of the AAAI/ACM Conference on AI Ethics and Society (1 paper)
- Partner nations
- United KingdomUnited StatesBelgium
In The Last Decade
Tom Stepleton
6 papers receiving 435 citations
Tom Stepleton's Hit Papers
Peers
Comparison fields: 5 of 75
- Health Informatics 57
- Safety Research 144
- General Social Sciences 20
- Artificial Intelligence 212
- Computer Science Applications 19
Countries citing papers authored by Tom Stepleton
This map shows the geographic impact of Tom Stepleton'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 Tom Stepleton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom Stepleton more than expected).
Fields of papers citing papers by Tom Stepleton
This network shows the impact of papers produced by Tom Stepleton. 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 Tom Stepleton. The network helps show where Tom Stepleton may publish in the future.
Co-authors
The 22 scholars most cited alongside Tom Stepleton, 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 | Taxonomy of Risks posed by Language Models Hit paper breakdown → | 2022 | 408 |
| 2 | 2020 | 24 | |
| 3 | 2024 | 12 | |
| 4 | Wasserstein Fair Classification | 2019 | 8 |
| 5 | 2009 | 2 | |
| 6 | 2016 | 2 |
About Tom Stepleton
Tom Stepleton is a scholar working on Safety Research, Artificial Intelligence, Software, Statistics and Probability and Industrial and Manufacturing Engineering, having authored 6 papers that have together received 456 indexed citations. Recurring topics across this work include Ethics and Social Impacts of AI (4 papers), Privacy-Preserving Technologies in Data (1 paper), Adversarial Robustness in Machine Learning (1 paper), Advanced Causal Inference Techniques (1 paper), Hate Speech and Cyberbullying Detection (1 paper), Industrial Vision Systems and Defect Detection (1 paper), Visual perception and processing mechanisms (1 paper) and Reinforcement Learning in Robotics (1 paper). The work is most often cited by research in Health Informatics (57 citations), Safety Research (144 citations), General Social Sciences (20 citations), Artificial Intelligence (212 citations) and Computer Science Applications (19 citations). Tom Stepleton has collaborated with scholars based in United Kingdom, United States and Belgium. Frequent co-authors include Heinrich Jiang, Aldo Pacchiano, Silvia Chiappa, John Aslanides, Jason M. Samonds, Nahema Marchal, Lisa Anne Hendricks, Arianna Manzini, Juan C Mateos-Garcia and Maribeth Rauh. Their work appears in journals such as Lecture notes in computer science, Uncertainty in Artificial Intelligence, Cambridge University Press eBooks, Proceedings of the AAAI Conference on Artificial Intelligence 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.