| FACE: Feasible and actionable counterfactual explanations R Poyiadzi, K Sokol, R Santos-Rodriguez, T De Bie, P Flach Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 344-350, 2020 | 438 | 2020 |
| Explainability fact sheets: a framework for systematic assessment of explainable approaches K Sokol, P Flach Proceedings of the 2020 Conference on Fairness, Accountability, and …, 2020 | 385 | 2020 |
| One Explanation Does Not Fit All K Sokol, P Flach KI-Künstliche Intelligenz, 1-16, 2020 | 204* | 2020 |
| Counterfactual Explanations of Machine Learning Predictions: Opportunities and Challenges for AI Safety K Sokol, PA Flach SafeAI 2019: AAAI Workshop on Artificial Intelligence Safety 2301 (urn:nbn …, 2019 | 113 | 2019 |
| Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant. K Sokol, PA Flach IJCAI, 5868-5870, 2018 | 86 | 2018 |
| bLIMEy: Surrogate Prediction Explanations Beyond LIME K Sokol, A Hepburn, R Santos-Rodriguez, P Flach 2019 Workshop on Human-Centric Machine Learning (HCML 2019) at the 33rd …, 2019 | 46 | 2019 |
| FAT Forensics: A Python toolbox for algorithmic fairness, accountability and transparency K Sokol, R Santos-Rodriguez, P Flach Software Impacts, 100406, 2022 | 44 | 2022 |
| LIMEtree: Consistent and Faithful Multi-class Explanations K Sokol, P Flach arXiv preprint arXiv:2005.01427, 2020 | 43* | 2020 |
| Conversational Explanations of Machine Learning Predictions Through Class-contrastive Counterfactual Statements. K Sokol, PA Flach IJCAI, 5785-5786, 2018 | 43 | 2018 |
| FAT Forensics: A Python toolbox for implementing and deploying fairness, accountability and transparency algorithms in predictive systems K Sokol, A Hepburn, R Poyiadzi, M Clifford, R Santos-Rodriguez, P Flach Journal of Open Source Software 5 (49), 1904, 2020 | 27 | 2020 |
| Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals K Sokol, P Flach Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 10035 …, 2019 | 26 | 2019 |
| Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence K Sokol, P Flach arXiv preprint arXiv:2112.14466, 2021 | 20 | 2021 |
| Releasing eHealth analytics into the wild: Lessons learnt from the SPHERE project T Diethe, M Holmes, M Kull, M Perello Nieto, K Sokol, H Song, E Tonkin, ... Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 18 | 2018 |
| Interpretable representations in explainable AI: From theory to practice K Sokol, P Flach Data Mining and Knowledge Discovery, 1-39, 2024 | 11* | 2024 |
| BayCon: Model-agnostic Bayesian Counterfactual Generator P Romashov, M Gjoreski, K Sokol, MV Martinez, M Langheinrich IJCAI, 740-746, 2022 | 11 | 2022 |
| Fairness, Accountability and Transparency in Artificial Intelligence: A Case Study of Logical Predictive Models K Sokol Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 541-542, 2019 | 9 | 2019 |
| Towards intelligible and robust surrogate explainers: a decision tree perspective K Sokol University of Bristol, 2021 | 7 | 2021 |
| (Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Comprehensibility K Sokol, JE Vogt Workshop on Interpretable Machine Learning in Healthcare (IMLH) at 2023 …, 2023 | 6* | 2023 |
| Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations E Small, Y Xuan, D Hettiachchi, K Sokol ACM CHI 2023 Workshop on Human-Centered Explainable AI (HCXAI), 2023 | 6 | 2023 |
| What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components K Sokol, A Hepburn, R Santos-Rodriguez, P Flach Journal of Open Source Education 5 (58), 175, 2022 | 6 | 2022 |
Peter FlachProfessor of Artificial Intelligence, Department of Computer Science, University of BristolEmail verificata su bristol.ac.uk
