Activity Recognition in Multiple Contexts for Smart-House Data. - Kacper Sokol; Peter A. Flach (ILP (Short Papers), 2016)

The Role of Textualisation and Argumentation in Understanding the Machine Learning Process. - Kacper Sokol; Peter A. Flach (IJCAI, 2017)

Conversational Explanations of Machine Learning Predictions Through Class-contrastive Counterfactual Statements. - Kacper Sokol; Peter A. Flach (IJCAI, 2018)

Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant. - Kacper Sokol; Peter A. Flach (IJCAI, 2018)

Releasing eHealth Analytics into the Wild: Lessons Learnt from the SPHERE Project. - Tom Diethe; Mike Holmes; Meelis Kull; Miquel Perelló-Nieto; Kacper Sokol; Hao Song 007; Emma Tonkin; Niall Twomey; Peter A. Flach (KDD, 2018)

Counterfactual Explanations of Machine Learning Predictions: Opportunities and Challenges for AI Safety. - Kacper Sokol; Peter A. Flach (SafeAI@AAAI, 2019)

Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals. - Kacper Sokol; Peter A. Flach (AAAI, 2019)

Fairness, Accountability and Transparency in Artificial Intelligence: A Case Study of Logical Predictive Models. - Kacper Sokol (AIES, 2019)

HyperStream: a Workflow Engine for Streaming Data. - Tom Diethe; Meelis Kull; Niall Twomey; Kacper Sokol; Hao Song 007; Miquel Perelló-Nieto; Emma Tonkin; Peter A. Flach (CoRR, 2019)

bLIMEy: Surrogate Prediction Explanations Beyond LIME. - Kacper Sokol; Alexander Hepburn; Raúl Santos-Rodríguez; Peter A. Flach (CoRR, 2019)

FACE: Feasible and Actionable Counterfactual Explanations. - Rafael Poyiadzi; Kacper Sokol; Raúl Santos-Rodríguez; Tijl De Bie; Peter A. Flach (AIES, 2020)

Explainability fact sheets: a framework for systematic assessment of explainable approaches. - Kacper Sokol; Peter A. Flach (FAT*, 2020)

One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency. - Kacper Sokol; Peter A. Flach (CoRR, 2020)

LIMEtree: Interactively Customisable Explanations Based on Local Surrogate Multi-output Regression Trees. - Kacper Sokol; Peter A. Flach (CoRR, 2020)

Towards Faithful and Meaningful Interpretable Representations. - Kacper Sokol; Peter A. Flach (CoRR, 2020)

FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems. - Kacper Sokol; Alexander Hepburn; Rafael Poyiadzi; Matthew Clifford; Raúl Santos-Rodríguez; Peter A. Flach (J. Open Source Softw., 2020)

One Explanation Does Not Fit All. - Kacper Sokol; Peter A. Flach (Künstliche Intell., 2020)

FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems. - Kacper Sokol; Alexander Hepburn; Rafael Poyiadzi; Matthew Clifford; Raúl Santos-Rodríguez; Peter A. Flach (Zenodo, 2020)

You Only Write Thrice: Creating Documents, Computational Notebooks and Presentations From a Single Source. - Kacper Sokol; Peter A. Flach (CoRR, 2021)

Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence. - Kacper Sokol; Peter A. Flach (CoRR, 2021)

BayCon: Model-agnostic Bayesian Counterfactual Generator. - Piotr Romashov; Martin Gjoreski; Kacper Sokol; Maria Vanina Martinez; Marc Langheinrich (IJCAI, 2022)

Ethical and Fairness Implications of Model Multiplicity. - Kacper Sokol; Meelis Kull; Jeffrey Chan; Flora Dilys Salim (CoRR, 2022)

How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies. - Edward Small; Wei Shao 006; Zeliang Zhang 001; Peihan Liu; Jeffrey Chan; Kacper Sokol; Flora D. Salim (CoRR, 2022)

Simply Logical - Intelligent Reasoning by Example (Fully Interactive Online Edition). - Peter A. Flach; Kacper Sokol (CoRR, 2022)

What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components. - Kacper Sokol; Alexander Hepburn; Raúl Santos-Rodríguez; Peter A. Flach (CoRR, 2022)

Analysing Donors' Behaviour in Non-profit Organisations for Disaster Resilience: The 2019-2020 Australian Bushfires Case Study. - Dilini Rajapaksha; Kacper Sokol; Jeffrey Chan; Flora D. Salim; Mukesh Prasad; Mahendra Samarawickrama (CoRR, 2022)

Simply Logical - Intelligent Reasoning by Example (Fully Interactive Online Edition). - Peter A. Flach; Kacper Sokol (Zenodo, 2022)

What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components. - Kacper Sokol; Alexander Hepburn; Raúl Santos-Rodriguez; Peter A. Flach (Zenodo, 2022)

FAT Forensics: A Python toolbox for algorithmic fairness, accountability and transparency. - Kacper Sokol; Raúl Santos-Rodríguez; Peter A. Flach (Softw. Impacts, 2022)

Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication. - Bernard Keenan; Kacper Sokol (CoRR, 2023)

Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations. - Edward Small; Yueqing Xuan; Danula Hettiachchi; Kacper Sokol (CoRR, 2023)

More Is Less: When Do Recommenders Underperform for Data-rich Users? - Yueqing Xuan; Kacper Sokol; Jeffrey Chan; Mark Sanderson (CoRR, 2023)

(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Explainability. - Kacper Sokol; Julia E. Vogt (CoRR, 2023)

Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse. - Edward A. Small; Jeffrey N. Clark; Christopher J. McWilliams; Kacper Sokol; Jeffrey Chan; Flora D. Salim; Raúl Santos-Rodríguez (CoRR, 2023)

Can Users Correctly Interpret Machine Learning Explanations and Simultaneously Identify Their Limitations? - Yueqing Xuan; Edward Small; Kacper Sokol; Danula Hettiachchi; Mark Sanderson (CoRR, 2023)

Simply Logical - The First Three Decades. - Peter A. Flach; Kacper Sokol; Jan Wielemaker (Prolog: The Next 50 Years, 2023)

What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks. - Kacper Sokol; Julia E. Vogt (CHI Extended Abstracts, 2024)

Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness. - Edward Small; Kacper Sokol; Daniel Manning; Flora D. Salim; Jeffrey Chan (FAccT, 2024)

Counterfactual Explanations for Clustering Models. - Aurora Spagnol; Kacper Sokol; Pietro Barbiero; Marc Langheinrich; Martin Gjoreski (CoRR, 2024)

Leveraging Simulation Data to Understand Bias in Predictive Models of Infectious Disease Spread. - Andreas Züfle; Flora D. Salim; Taylor Anderson 001; Matthew Scotch; Li Xiong 001; Kacper Sokol; Hao Xue 001; Ruochen Kong; David J. Heslop; Hye-Young Paik; C. Raina MacIntyre (ACM Trans. Spatial Algorithms Syst., 2024)

Interpretable representations in explainable AI: from theory to practice. - Kacper Sokol; Peter A. Flach (Data Min. Knowl. Discov., 2024)

Leveraging Complementary AI Explanations to Mitigate Misunderstanding in XAI. - Yueqing Xuan; Kacper Sokol; Mark Sanderson; Jeffrey Chan (SDS, 2025)

Comprehension is a double-edged sword: Over-interpreting unspecified information in intelligible machine learning explanations. - Yueqing Xuan; Edward Small; Kacper Sokol; Danula Hettiachchi; Mark Sanderson (Int. J. Hum. Comput. Stud., 2025)

Artificial intelligence should genuinely support clinical reasoning and decision making to bridge the translational gap. - Kacper Sokol; James C. Fackler; Julia E. Vogt (npj Digit. Medicine, 2025)

From Research to Certification with Data-Driven Medical Decision Support Systems (Dagstuhl Seminar 25052). - Raúl Santos-Rodríguez; Kacper Sokol; Julia E. Vogt; Sven Wellmann (Dagstuhl Reports, 2025)

All You Need for Counterfactual Explainability Is Principled and Reliable Estimate of Aleatoric and Epistemic Uncertainty. - Kacper Sokol; Eyke Hüllermeier (CoRR, 2025)

Navigating explanatory multiverse through counterfactual path geometry. - Kacper Sokol; Edward Small; Yueqing Xuan (Mach. Learn., 2025)

How robust is your fair model? Exploring the robustness of prominent fairness strategies. - Edward A. Small; Wei Shao 006; Zeliang Zhang 001; Peihan Liu; Jeffrey Chan; Kacper Sokol; Flora D. Salim (Data Min. Knowl. Discov., 2025)

Diverse Negative Sampling for Implicit Collaborative Filtering. - Yueqing Xuan; Kacper Sokol; Mark Sanderson; Jeffrey Chan (CoRR, 2025)

Perfect counterfactuals in imperfect worlds: modelling noisy implementation of actions in sequential algorithmic recourse. - Yueqing Xuan; Kacper Sokol; Mark Sanderson; Jeffrey Chan (Mach. Learn., 2025)