Kacper Sokol

Kacper Sokol

Postdoc
Università della Svizzera italiana

Kacper Sokol is a postdoctoral researcher at the Faculty of Informatics, Università della Svizzera italiana (USI), affiliated with the People-Centered Computing Lab.

Research interests

His research primarily focuses on the explainability of data-driven predictive systems based on artificial intelligence and machine-learning algorithms, with a particular interest in their applications within the medical field.

Publications
Edward Small, Yueqing Xuan, Danula Hettiachchi, Kacper Sokol (2023). Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations. CoRR.
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Bernard Keenan, Kacper Sokol (2023). Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication. CoRR.
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Yueqing Xuan, Kacper Sokol, Jeffrey Chan, Mark Sanderson (2023). More Is Less: When Do Recommenders Underperform for Data-rich Users?. CoRR.
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Kacper Sokol, Edward Small, Yueqing Xuan (2023). Navigating Explanatory Multiverse Through Counterfactual Path Geometry. CoRR.
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Peter A. Flach, Kacper Sokol, Jan Wielemaker (2023). Simply Logical - The First Three Decades. Prolog: The Next 50 Years.
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Kacper Sokol, Alexander Hepburn, Raúl Santos-Rodriguez, Peter A. Flach (2022). What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components (Version 2022-jose). Zenodo.
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Peter A. Flach, Kacper Sokol (2022). Simply Logical - Intelligent Reasoning by Example (Fully Interactive Online Edition) (Version v2.0). Zenodo.
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Dilini Rajapaksha, Kacper Sokol, Jeffrey Chan, Flora D. Salim, Mukesh Prasad, Mahendra Samarawickrama (2022). Analysing Donors' Behaviour in Non-profit Organisations for Disaster Resilience: The 2019-2020 Australian Bushfires Case Study. CoRR.
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Piotr Romashov, Martin Gjoreski, Kacper Sokol, Maria Vanina Martinez, Marc Langheinrich (2022). BayCon: Model-agnostic Bayesian Counterfactual Generator. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022.
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Kacper Sokol, Meelis Kull, Jeffrey Chan, Flora Dilys Salim (2022). Ethical and Fairness Implications of Model Multiplicity. CoRR.
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Kacper Sokol, Raúl Santos-Rodríguez, Peter A. Flach (2022). FAT Forensics: A Python toolbox for algorithmic fairness, accountability and transparency. Softw. Impacts.
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Kacper Sokol, Alexander Hepburn, Rafael Poyiadzi, Matthew Clifford, Raúl Santos-Rodríguez, Peter A. Flach (2022). FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems. CoRR.
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Edward Small, Wei Shao, Zeliang Zhang, Peihan Liu, Jeffrey Chan, Kacper Sokol, Flora D. Salim (2022). How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies. CoRR.
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Peter A. Flach, Kacper Sokol (2022). Simply Logical - Intelligent Reasoning by Example (Fully Interactive Online Edition). CoRR.
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Kacper Sokol, Alexander Hepburn, Raúl Santos-Rodríguez, Peter A. Flach (2022). What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components. CoRR.
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