Kacper Sokol

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 42

Top Topics

Artificial Intelligence

Regression Trees

Infectious Disease

Top Venues

Publications

Andreas Züfle, Flora D. Salim, Taylor Anderson, Matthew Scotch, Li Xiong, Kacper Sokol, Hao Xue, Ruochen Kong, David J. Heslop, Hye-Young Paik, C. Raina MacIntyre
Leveraging Simulation Data to Understand Bias in Predictive Models of Infectious Disease Spread. ACM Trans. Spatial Algorithms Syst. 10 (2) (2024)
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 (2024)
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. CHI Extended Abstracts (2024)
Edward Small, Kacper Sokol, Daniel Manning, Flora D. Salim, Jeffrey Chan
Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness. FAccT (2024)
Kacper Sokol, Edward Small, Yueqing Xuan
Navigating Explanatory Multiverse Through Counterfactual Path Geometry. CoRR (2023)
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 (2023)
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 (2023)
Yueqing Xuan, Edward Small, Kacper Sokol, Danula Hettiachchi, Mark Sanderson
Can Users Correctly Interpret Machine Learning Explanations and Simultaneously Identify Their Limitations? CoRR (2023)
Edward Small, Yueqing Xuan, Danula Hettiachchi, Kacper Sokol
Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations. CoRR (2023)
Bernard Keenan, Kacper Sokol
Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication. CoRR (2023)
Yueqing Xuan, Kacper Sokol, Jeffrey Chan, Mark Sanderson
More Is Less: When Do Recommenders Underperform for Data-rich Users? CoRR (2023)
Kacper Sokol, Julia E. Vogt
(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Explainability. CoRR (2023)
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 (2022)
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 (2022)
Peter A. Flach, Kacper Sokol
Simply Logical - Intelligent Reasoning by Example (Fully Interactive Online Edition). CoRR (2022)
Kacper Sokol, Alexander Hepburn, Raúl Santos-Rodríguez, Peter Flach
What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components. CoRR (2022)
Kacper Sokol, Meelis Kull, Jeffrey Chan, Flora Dilys Salim
Ethical and Fairness Implications of Model Multiplicity. CoRR (2022)
Kacper Sokol, Raúl Santos-Rodríguez, Peter A. Flach
FAT Forensics: A Python toolbox for algorithmic fairness, accountability and transparency. Softw. Impacts 14 (2022)
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 (2022)
Piotr Romashov, Martin Gjoreski, Kacper Sokol, Maria Vanina Martinez, Marc Langheinrich
BayCon: Model-agnostic Bayesian Counterfactual Generator. IJCAI (2022)
Kacper Sokol, Peter A. Flach
You Only Write Thrice: Creating Documents, Computational Notebooks and Presentations From a Single Source. CoRR (2021)
Kacper Sokol, Peter A. Flach
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence. CoRR (2021)
Rafael Poyiadzi, Kacper Sokol, Raúl Santos-Rodríguez, Tijl De Bie, Peter A. Flach
FACE: Feasible and Actionable Counterfactual Explanations. AIES (2020)
Kacper Sokol, Peter A. Flach
Towards Faithful and Meaningful Interpretable Representations. CoRR (2020)
Kacper Sokol, Peter A. Flach
One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency. CoRR (2020)
Kacper Sokol, Peter A. Flach
LIMEtree: Interactively Customisable Explanations Based on Local Surrogate Multi-output Regression Trees. CoRR (2020)
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) (2020)
Kacper Sokol, Peter A. Flach
One Explanation Does Not Fit All. Künstliche Intell. 34 (2) (2020)
Kacper Sokol, Peter A. Flach
Explainability fact sheets: a framework for systematic assessment of explainable approaches. FAT* (2020)
Kacper Sokol, Alexander Hepburn, Raúl Santos-Rodríguez, Peter A. Flach
bLIMEy: Surrogate Prediction Explanations Beyond LIME. CoRR (2019)
Rafael Poyiadzi, Kacper Sokol, Raúl Santos-Rodriguez, Tijl De Bie, Peter A. Flach
FACE: Feasible and Actionable Counterfactual Explanations. CoRR (2019)
Kacper Sokol, Peter A. Flach
Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches. CoRR (2019)
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 (2019)
Kacper Sokol, Peter A. Flach
Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals. AAAI (2019)
Kacper Sokol, Raúl Santos-Rodríguez, Peter A. Flach
FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency. CoRR (2019)
Kacper Sokol
Fairness, Accountability and Transparency in Artificial Intelligence: A Case Study of Logical Predictive Models. AIES (2019)
Kacper Sokol, Peter A. Flach
Counterfactual Explanations of Machine Learning Predictions: Opportunities and Challenges for AI Safety. SafeAI@AAAI (2019)
Kacper Sokol, Peter A. Flach
Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant. IJCAI (2018)
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)
Kacper Sokol, Peter A. Flach
Conversational Explanations of Machine Learning Predictions Through Class-contrastive Counterfactual Statements. IJCAI (2018)
Kacper Sokol, Peter A. Flach
The Role of Textualisation and Argumentation in Understanding the Machine Learning Process. IJCAI (2017)
Kacper Sokol, Peter A. Flach
Activity Recognition in Multiple Contexts for Smart-House Data. ILP (Short Papers) (2016)