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Kacper Sokol
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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
1412020
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
1292020
One Explanation Does Not Fit All
K Sokol, P Flach
KI-Künstliche Intelligenz, 1-16, 2020
532020
Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant.
K Sokol, PA Flach
IJCAI, 5868-5870, 2018
412018
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
342019
Conversational Explanations of Machine Learning Predictions Through Class-contrastive Counterfactual Statements.
K Sokol, PA Flach
IJCAI, 5785-5786, 2018
262018
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
19*2019
FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency
K Sokol, R Santos-Rodriguez, P Flach
arXiv preprint arXiv:1909.05167, 2019
162019
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
152020
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
152018
LIMEtree: Interactively Customisable Explanations Based on Local Surrogate Multi-output Regression Trees
K Sokol, P Flach
arXiv preprint arXiv:2005.01427, 2020
122020
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
122019
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
22019
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
12021
Towards Faithful and Meaningful Interpretable Representations
K Sokol, P Flach
arXiv preprint arXiv:2008.07007, 2020
12020
The Role of Textualisation and Argumentation in Understanding the Machine Learning Process.
K Sokol, PA Flach
IJCAI, 5211-5212, 2017
12017
Ethical and Fairness Implications of Model Multiplicity
K Sokol, M Kull, J Chan, FD Salim
arXiv preprint arXiv:2203.07139, 2022
2022
You Only Write Thrice: Creating Documents, Computational Notebooks and Presentations From a Single Source
K Sokol, P Flach
Beyond static papers: Rethinking how we share scientific understanding in ML …, 2021
2021
Towards intelligible and robust surrogate explainers: a decision tree perspective
K Sokol
University of Bristol, 2021
2021
What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components
K Sokol, A Hepburn, R Santos-Rodriguez, P Flach
https://zenodo.org/record/4035128, 2020
2020
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Articles 1–20