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ORCIDKacper Sokol
Person information
- affiliation: ETH Zurich, Switzerland
- affiliation: University of Bristol, Intelligent Systems Laboratory, UK
- affiliation (former): RMIT University, ARC Centre of Excellence for Automated Decision-Making and Society, Australia
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2020 – today
- 2024
[i25]Kacper Sokol, Julia E. Vogt:
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks. CoRR abs/2403.12730 (2024)- 2023
- [p1]Peter A. Flach
, Kacper Sokol, Jan Wielemaker:
Simply Logical - The First Three Decades. Prolog: The Next 50 Years 2023: 184-193 - [i24]Bernard Keenan, Kacper Sokol:
Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication. CoRR abs/2302.03460 (2023) - [i23]Edward Small, Yueqing Xuan, Danula Hettiachchi, Kacper Sokol:
Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations. CoRR abs/2303.00934 (2023) - [i22]Yueqing Xuan, Kacper Sokol, Jeffrey Chan, Mark Sanderson:
More Is Less: When Do Recommenders Underperform for Data-rich Users? CoRR abs/2304.07487 (2023) - [i21]Edward A. Small, Kacper Sokol, Daniel Manning, Flora D. Salim, Jeffrey Chan:
Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness. CoRR abs/2304.09779 (2023) - [i20]Kacper Sokol, Julia E. Vogt:
(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Explainability. CoRR abs/2306.02312 (2023) - [i19]Kacper Sokol, Edward Small, Yueqing Xuan:
Navigating Explanatory Multiverse Through Counterfactual Path Geometry. CoRR abs/2306.02786 (2023) - [i18]Edward A. Small, Jeffrey N. Clark, Christopher J. McWilliams, Kacper Sokol, Jeffrey Chan, Flora D. Salim, Raúl Santos-Rodríguez:
Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse. CoRR abs/2309.04211 (2023) - [i17]Yueqing Xuan, Edward Small, Kacper Sokol, Danula Hettiachchi, Mark Sanderson:
Can Users Correctly Interpret Machine Learning Explanations and Simultaneously Identify Their Limitations? CoRR abs/2309.08438 (2023) - 2022
- [j3]Kacper Sokol, Raúl Santos-Rodríguez, Peter A. Flach:
FAT Forensics: A Python toolbox for algorithmic fairness, accountability and transparency. Softw. Impacts 14: 100406 (2022) - [c11]Piotr Romashov, Martin Gjoreski, Kacper Sokol, Maria Vanina Martinez, Marc Langheinrich:
BayCon: Model-agnostic Bayesian Counterfactual Generator. IJCAI 2022: 740-746 - [i16]Kacper Sokol, Meelis Kull, Jeffrey Chan, Flora Dilys Salim:
Ethical and Fairness Implications of Model Multiplicity. CoRR abs/2203.07139 (2022) - [i15]Edward Small, Wei Shao, Zeliang Zhang, Peihan Liu, Jeffrey Chan, Kacper Sokol, Flora D. Salim:
How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies. CoRR abs/2207.04581 (2022) - [i14]Peter A. Flach, Kacper Sokol:
Simply Logical - Intelligent Reasoning by Example (Fully Interactive Online Edition). CoRR abs/2208.06823 (2022) - [i13]Kacper Sokol, Alexander Hepburn, Rafael Poyiadzi, Matthew Clifford, Raúl Santos-Rodríguez, Peter A. Flach:
FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems. CoRR abs/2209.03805 (2022) - [i12]Kacper Sokol, Alexander Hepburn, Raúl Santos-Rodríguez, Peter A. Flach:
What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components. CoRR abs/2209.03813 (2022) - [i11]Dilini Rajapaksha, Kacper Sokol, Jeffrey Chan, Flora D. Salim, Mukesh Prasad, Mahendra Samarawickrama:
Analysing Donors' Behaviour in Non-profit Organisations for Disaster Resilience: The 2019-2020 Australian Bushfires Case Study. CoRR abs/2210.09034 (2022) - 2021
- [i10]Kacper Sokol, Peter A. Flach:
You Only Write Thrice: Creating Documents, Computational Notebooks and Presentations From a Single Source. CoRR abs/2107.06639 (2021) - [i9]Kacper Sokol, Peter A. Flach:
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence. CoRR abs/2112.14466 (2021) - 2020
- [j2]Kacper Sokol, Alexander Hepburn, Rafael Poyiadzi, Matthew Clifford, Raúl Santos-Rodríguez, Peter A. Flach:
FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems. J. Open Source Softw. 5(49): 1904 (2020) - [j1]Kacper Sokol, Peter A. Flach:
One Explanation Does Not Fit All. Künstliche Intell. 34(2): 235-250 (2020) - [c10]Rafael Poyiadzi, Kacper Sokol, Raúl Santos-Rodríguez, Tijl De Bie, Peter A. Flach:
FACE: Feasible and Actionable Counterfactual Explanations. AIES 2020: 344-350 - [c9]Kacper Sokol, Peter A. Flach:
Explainability fact sheets: a framework for systematic assessment of explainable approaches. FAT* 2020: 56-67 - [i8]Kacper Sokol, Peter A. Flach:
One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency. CoRR abs/2001.09734 (2020) - [i7]Kacper Sokol, Peter A. Flach:
LIMEtree: Interactively Customisable Explanations Based on Local Surrogate Multi-output Regression Trees. CoRR abs/2005.01427 (2020) - [i6]Kacper Sokol, Peter A. Flach:
Towards Faithful and Meaningful Interpretable Representations. CoRR abs/2008.07007 (2020)
2010 – 2019
- 2019
- [c8]Kacper Sokol, Peter A. Flach:
Counterfactual Explanations of Machine Learning Predictions: Opportunities and Challenges for AI Safety. SafeAI@AAAI 2019 - [c7]Kacper Sokol, Peter A. Flach:
Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals. AAAI 2019: 10035-10036 - [c6]Kacper Sokol:
Fairness, Accountability and Transparency in Artificial Intelligence: A Case Study of Logical Predictive Models. AIES 2019: 541-542 - [i5]Tom Diethe, Meelis Kull, Niall Twomey, Kacper Sokol, Hao Song, Miquel Perelló-Nieto, Emma Tonkin, Peter A. Flach:
HyperStream: a Workflow Engine for Streaming Data. CoRR abs/1908.02858 (2019) - [i4]Kacper Sokol, Raúl Santos-Rodríguez, Peter A. Flach:
FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency. CoRR abs/1909.05167 (2019) - [i3]Rafael Poyiadzi, Kacper Sokol, Raúl Santos-Rodriguez, Tijl De Bie, Peter A. Flach:
FACE: Feasible and Actionable Counterfactual Explanations. CoRR abs/1909.09369 (2019) - [i2]Kacper Sokol, Alexander Hepburn, Raúl Santos-Rodríguez, Peter A. Flach:
bLIMEy: Surrogate Prediction Explanations Beyond LIME. CoRR abs/1910.13016 (2019) - [i1]Kacper Sokol, Peter A. Flach:
Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches. CoRR abs/1912.05100 (2019) - 2018
- [c5]Kacper Sokol, Peter A. Flach:
Conversational Explanations of Machine Learning Predictions Through Class-contrastive Counterfactual Statements. IJCAI 2018: 5785-5786 - [c4]Kacper Sokol, Peter A. Flach:
Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant. IJCAI 2018: 5868-5870 - [c3]Tom Diethe, Mike Holmes, Meelis Kull, Miquel Perelló-Nieto, Kacper Sokol, Hao Song, Emma Tonkin, Niall Twomey, Peter A. Flach:
Releasing eHealth Analytics into the Wild: Lessons Learnt from the SPHERE Project. KDD 2018: 243-252 - 2017
- [c2]Kacper Sokol, Peter A. Flach:
The Role of Textualisation and Argumentation in Understanding the Machine Learning Process. IJCAI 2017: 5211-5212 - 2016
- [c1]Kacper Sokol, Peter A. Flach:
Activity Recognition in Multiple Contexts for Smart-House Data. ILP (Short Papers) 2016: 66-72
Coauthor Index
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