Kacper Sokol - RankMe

60 papers A* 7A 1C 2Journal 42Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	Diversity-Augmented Negative Sampling for Implicit Collaborative Filtering. WWW Yueqing Xuan, Kacper Sokol, Mark Sanderson, Jeffrey Chan
2025	J	`jnl`	All You Need for Counterfactual Explainability Is Principled and Reliable Estimate of Aleatoric and Epistemic Uncertainty. CoRR Kacper Sokol, Eyke Hüllermeier
2025	J	`jnl`	Artificial Intelligence Should Genuinely Support Clinical Reasoning and Decision Making To Bridge the Translational Gap. CoRR Kacper Sokol, James C. Fackler, Julia E. Vogt
2025	J	`jnl`	Artificial intelligence should genuinely support clinical reasoning and decision making to bridge the translational gap. npj Digit. Medicine Kacper Sokol, James C. Fackler, Julia E. Vogt
2025	J	`jnl`	Comprehension is a double-edged sword: Over-interpreting unspecified information in intelligible machine learning explanations. Int. J. Hum. Comput. Stud. Yueqing Xuan, Edward Small, Kacper Sokol, Danula Hettiachchi, Mark Sanderson
2025	J	`jnl`	Diverse Negative Sampling for Implicit Collaborative Filtering. CoRR Yueqing Xuan, Kacper Sokol, Mark Sanderson, Jeffrey Chan
2025	A	`conf`	Evaluating and Addressing Fairness Across User Groups in Negative Sampling for Recommender Systems. CIKM Yueqing Xuan, Kacper Sokol, Mark Sanderson, Jeffrey Chan
2025	J	`jnl`	From Research to Certification with Data-Driven Medical Decision Support Systems (Dagstuhl Seminar 25052). Dagstuhl Reports Raúl Santos-Rodríguez, Kacper Sokol, Julia E. Vogt, Sven Wellmann
2025	J	`jnl`	How robust is your fair model? Exploring the robustness of prominent fairness strategies. Data Min. Knowl. Discov. Edward A. Small, Wei Shao, Zeliang Zhang, Peihan Liu, Jeffrey Chan, Kacper Sokol, Flora D. Salim
2025	—	`conf`	Leveraging Complementary AI Explanations to Mitigate Misunderstanding in XAI. SDS Yueqing Xuan, Kacper Sokol, Mark Sanderson, Jeffrey Chan
2025	J	`jnl`	Leveraging Complementary AI Explanations to Mitigate Misunderstanding in XAI. CoRR Yueqing Xuan, Kacper Sokol, Mark Sanderson, Jeffrey Chan
2025	J	`jnl`	Navigating explanatory multiverse through counterfactual path geometry. Mach. Learn. Kacper Sokol, Edward Small, Yueqing Xuan
2025	J	`jnl`	Perfect counterfactuals in imperfect worlds: modelling noisy implementation of actions in sequential algorithmic recourse. Mach. Learn. Yueqing Xuan, Kacper Sokol, Mark Sanderson, Jeffrey Chan
2024	J	`jnl`	Counterfactual Explanations for Clustering Models. CoRR Aurora Spagnol, Kacper Sokol, Pietro Barbiero, Marc Langheinrich, Martin Gjoreski
2024	—	`conf`	Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness. FAccT Edward Small, Kacper Sokol, Daniel Manning, Flora D. Salim, Jeffrey Chan
2024	J	`jnl`	Interpretable representations in explainable AI: from theory to practice. Data Min. Knowl. Discov. Kacper Sokol, Peter A. Flach
2024	J	`jnl`	Leveraging Simulation Data to Understand Bias in Predictive Models of Infectious Disease Spread. ACM Trans. Spatial Algorithms Syst. 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
2024	J	`jnl`	Perfect Counterfactuals in Imperfect Worlds: Modelling Noisy Implementation of Actions in Sequential Algorithmic Recourse. CoRR Yueqing Xuan, Kacper Sokol, Mark Sanderson, Jeffrey Chan
2024	—	`ed.`	Proceedings of the First Multimodal, Affective and Interactive eXplainable AI Workshop (MAI-XAI24 2024) co-located with 27th European Conference On Artificial Intelligence 19-24 October 2024 (ECAI 2024), Santiago de Compostela, Spain, October 19, 2024. MAI-XAI@ECAI Jose Maria Alonso-Moral, Zach Anthis, Rafael Berlanga, Alejandro Catalá, Philipp Cimiano, Peter Flach, Eyke Hüllermeier, Tim Miller, Oana Mitrut, Dimitry Mindlin, Gabriela Moise, Alin Moldoveanu, Florica Moldoveanu, Kacper Sokol, Aitor Soroa
2024	—	`conf`	What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks. CHI Extended Abstracts Kacper Sokol, Julia E. Vogt
2024	J	`jnl`	What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks. CoRR Kacper Sokol, Julia E. Vogt
2023	J	`jnl`	(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Explainability. CoRR Kacper Sokol, Julia E. Vogt
2023	J	`jnl`	Can Users Correctly Interpret Machine Learning Explanations and Simultaneously Identify Their Limitations? CoRR Yueqing Xuan, Edward Small, Kacper Sokol, Danula Hettiachchi, Mark Sanderson
2023	J	`jnl`	Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse. CoRR Edward A. Small, Jeffrey N. Clark, Christopher J. McWilliams, Kacper Sokol, Jeffrey Chan, Flora D. Salim, Raúl Santos-Rodríguez
2023	J	`jnl`	Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness. CoRR Edward A. Small, Kacper Sokol, Daniel Manning, Flora D. Salim, Jeffrey Chan
2023	J	`jnl`	Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations. CoRR Edward Small, Yueqing Xuan, Danula Hettiachchi, Kacper Sokol
2023	J	`jnl`	Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication. CoRR Bernard Keenan, Kacper Sokol
2023	J	`jnl`	More Is Less: When Do Recommenders Underperform for Data-rich Users? CoRR Yueqing Xuan, Kacper Sokol, Jeffrey Chan, Mark Sanderson
2023	J	`jnl`	Navigating Explanatory Multiverse Through Counterfactual Path Geometry. CoRR Kacper Sokol, Edward Small, Yueqing Xuan
2023	—	`ch.`	Simply Logical - The First Three Decades. Prolog: The Next 50 Years Peter A. Flach, Kacper Sokol, Jan Wielemaker
2022	J	`jnl`	Analysing Donors' Behaviour in Non-profit Organisations for Disaster Resilience: The 2019-2020 Australian Bushfires Case Study. CoRR Dilini Rajapaksha, Kacper Sokol, Jeffrey Chan, Flora D. Salim, Mukesh Prasad, Mahendra Samarawickrama
2022	A*	`conf`	BayCon: Model-agnostic Bayesian Counterfactual Generator. IJCAI Piotr Romashov, Martin Gjoreski, Kacper Sokol, Maria Vanina Martinez, Marc Langheinrich
2022	J	`jnl`	Ethical and Fairness Implications of Model Multiplicity. CoRR Kacper Sokol, Meelis Kull, Jeffrey Chan, Flora Dilys Salim
2022	J	`jnl`	FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems. CoRR Kacper Sokol, Alexander Hepburn, Rafael Poyiadzi, Matthew Clifford, Raúl Santos-Rodríguez, Peter A. Flach
2022	J	`jnl`	FAT Forensics: A Python toolbox for algorithmic fairness, accountability and transparency. Softw. Impacts Kacper Sokol, Raúl Santos-Rodríguez, Peter A. Flach
2022	J	`jnl`	How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies. CoRR Edward Small, Wei Shao, Zeliang Zhang, Peihan Liu, Jeffrey Chan, Kacper Sokol, Flora D. Salim
2022	J	`jnl`	Simply Logical - Intelligent Reasoning by Example (Fully Interactive Online Edition). CoRR Peter A. Flach, Kacper Sokol
2022	J	`jnl`	What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components. CoRR Kacper Sokol, Alexander Hepburn, Raúl Santos-Rodríguez, Peter A. Flach
2021	J	`jnl`	Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence. CoRR Kacper Sokol, Peter A. Flach
2021	J	`jnl`	You Only Write Thrice: Creating Documents, Computational Notebooks and Presentations From a Single Source. CoRR Kacper Sokol, Peter A. Flach
2020	—	`conf`	Explainability fact sheets: a framework for systematic assessment of explainable approaches. FAT* Kacper Sokol, Peter A. Flach
2020	C	`conf`	FACE: Feasible and Actionable Counterfactual Explanations. AIES Rafael Poyiadzi, Kacper Sokol, Raúl Santos-Rodríguez, Tijl De Bie, Peter A. Flach
2020	J	`jnl`	FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems. J. Open Source Softw. Kacper Sokol, Alexander Hepburn, Rafael Poyiadzi, Matthew Clifford, Raúl Santos-Rodríguez, Peter A. Flach
2020	J	`jnl`	LIMEtree: Interactively Customisable Explanations Based on Local Surrogate Multi-output Regression Trees. CoRR Kacper Sokol, Peter A. Flach
2020	J	`jnl`	One Explanation Does Not Fit All. Künstliche Intell. Kacper Sokol, Peter A. Flach
2020	J	`jnl`	One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency. CoRR Kacper Sokol, Peter A. Flach
2020	J	`jnl`	Towards Faithful and Meaningful Interpretable Representations. CoRR Kacper Sokol, Peter A. Flach
2019	—	`conf`	Counterfactual Explanations of Machine Learning Predictions: Opportunities and Challenges for AI Safety. SafeAI@AAAI Kacper Sokol, Peter A. Flach
2019	A*	`conf`	Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals. AAAI Kacper Sokol, Peter A. Flach
2019	J	`jnl`	Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches. CoRR Kacper Sokol, Peter A. Flach
2019	J	`jnl`	FACE: Feasible and Actionable Counterfactual Explanations. CoRR Rafael Poyiadzi, Kacper Sokol, Raúl Santos-Rodriguez, Tijl De Bie, Peter A. Flach
2019	J	`jnl`	FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency. CoRR Kacper Sokol, Raúl Santos-Rodríguez, Peter A. Flach
2019	C	`conf`	Fairness, Accountability and Transparency in Artificial Intelligence: A Case Study of Logical Predictive Models. AIES Kacper Sokol
2019	J	`jnl`	HyperStream: a Workflow Engine for Streaming Data. CoRR Tom Diethe, Meelis Kull, Niall Twomey, Kacper Sokol, Hao Song, Miquel Perelló-Nieto, Emma Tonkin, Peter A. Flach
2019	J	`jnl`	bLIMEy: Surrogate Prediction Explanations Beyond LIME. CoRR Kacper Sokol, Alexander Hepburn, Raúl Santos-Rodríguez, Peter A. Flach
2018	A*	`conf`	Conversational Explanations of Machine Learning Predictions Through Class-contrastive Counterfactual Statements. IJCAI Kacper Sokol, Peter A. Flach
2018	A*	`conf`	Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant. IJCAI Kacper Sokol, Peter A. Flach
2018	A*	`conf`	Releasing eHealth Analytics into the Wild: Lessons Learnt from the SPHERE Project. KDD Tom Diethe, Mike Holmes, Meelis Kull, Miquel Perelló-Nieto, Kacper Sokol, Hao Song, Emma Tonkin, Niall Twomey, Peter A. Flach
2017	A*	`conf`	The Role of Textualisation and Argumentation in Understanding the Machine Learning Process. IJCAI Kacper Sokol, Peter A. Flach
2016	—	`conf`	Activity Recognition in Multiple Contexts for Smart-House Data. ILP (Short Papers) Kacper Sokol, Peter A. Flach