Kacper Sokol
- Artificial Intelligence top 5%
- Safety Research top 10%
- Information Systems and Management top 10%
- Health Informatics top 5%
- Computer Vision and Pattern Recognition
- Co-authors
- Peter FlachRaúl Santos‐RodríguezMatthew CliffordJames C. FacklerJulia E. VogtMarc LangheinrichJeffrey ChanHao Song
- Topics
- Explainable Artificial Intelligence (XAI) (17 papers)Adversarial Robustness in Machine Learning (11 papers)Ethics and Social Impacts of AI (7 papers)
- Partner nations
- United KingdomSwitzerlandAustralia
In The Last Decade
Kacper Sokol
22 papers receiving 280 citations
Peers
Comparison fields: 5 of 73
- Artificial Intelligence 227
- Safety Research 62
- Information Systems and Management 54
- Health Informatics 49
- Computer Vision and Pattern Recognition 25
Countries citing papers authored by Kacper Sokol
This map shows the geographic impact of Kacper Sokol'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 Kacper Sokol with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kacper Sokol more than expected).
Fields of papers citing papers by Kacper Sokol
This network shows the impact of papers produced by Kacper Sokol. 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 Kacper Sokol. The network helps show where Kacper Sokol may publish in the future.
Co-authorship network of co-authors of Kacper Sokol
This figure shows the co-authorship network connecting the top 25 collaborators of Kacper Sokol. A scholar is included among the top collaborators of Kacper Sokol based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kacper Sokol. Kacper Sokol is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 8 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 1 | |
| 7 | 2 | |
| 8 | 4 | |
| 9 | 5 | |
| 10 | 15 | |
| 11 | 5 | |
| 12 | 3 | |
| 13 | 12 | |
| 14 | 24 | |
| 15 | 92 | |
| 16 | Counterfactual explanations of machine learning predictions: Opportunities and challenges for AI safety | 23 |
| 17 | 6 | |
| 18 | 38 | |
| 19 | 11 | |
| 20 | 1 |
About Kacper Sokol
Kacper Sokol is a scholar working on Health Informatics, Safety Research and Information Systems and Management, having authored 23 papers that have together received 286 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (17 papers), Adversarial Robustness in Machine Learning (11 papers) and Ethics and Social Impacts of AI (7 papers). The work is most often cited by research in Health Informatics (49 citations), Information Systems and Management (54 citations) and Safety Research (62 citations). Kacper Sokol has collaborated with scholars based in United Kingdom, Switzerland and Australia. Frequent co-authors include Peter Flach, Raúl Santos‐Rodríguez, Matthew Clifford, James C. Fackler, Julia E. Vogt, Marc Langheinrich, Jeffrey Chan, Hao Song, Tom Diethe and Martin Gjoreski. Their work appears in journals such as Machine Learning, International Journal of Human-Computer Studies and npj Digital Medicine.
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.