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Kacper Sokol
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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
4422020
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
3882020
One Explanation Does Not Fit All
K Sokol, P Flach
KI-Künstliche Intelligenz, 1-16, 2020
209*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
1132019
Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant.
K Sokol, PA Flach
IJCAI, 5868-5870, 2018
862018
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
462019
FAT Forensics: A Python toolbox for algorithmic fairness, accountability and transparency
K Sokol, R Santos-Rodriguez, P Flach
Software Impacts, 100406, 2022
452022
Conversational Explanations of Machine Learning Predictions Through Class-contrastive Counterfactual Statements.
K Sokol, PA Flach
IJCAI, 5785-5786, 2018
442018
LIMEtree: Consistent and Faithful Multi-class Explanations
K Sokol, P Flach
arXiv preprint arXiv:2005.01427, 2020
43*2020
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
282020
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
262019
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
202021
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
182018
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
112022
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
92019
Towards intelligible and robust surrogate explainers: a decision tree perspective
K Sokol
University of Bristol, 2021
72021
What does evaluation of explainable artificial intelligence actually tell us? A case for compositional and contextual validation of XAI building blocks
K Sokol, JE Vogt
Extended Abstracts of the CHI Conference on Human Factors in Computing …, 2024
62024
(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
62023
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