13 citations · 25 across the 12 of their papers we have counts for
5 papers · 1 filter
Actionable and diverse counterfactual explanations incorporating domain knowledge and plausibility constraints
Szymon Bobek, Łukasz Bałec, Grzegorz J. Nalepa
Counterfactual explanations improve the actionable interpretability of machine learning models by identifying minimal changes required to achieve a desired outcome. However, existi…
User-centric evaluation of explainability of AI with and for humans: a comprehensive empirical study
Szymon Bobek, Paloma Korycińska, Monika Krakowska +5
This study is located in the Human-Centered Artificial Intelligence (HCAI) and focuses on the results of a user-centered assessment of commonly used eXplainable Artificial Intellig…
Artificial Intelligence Approaches for Predictive Maintenance in the Steel Industry: A Survey
Jakub Jakubowski, Natalia Wojak-Strzelecka, Rita P. Ribeiro +4
Predictive Maintenance (PdM) emerged as one of the pillars of Industry 4.0, and became crucial for enhancing operational efficiency, allowing to minimize downtime, extend lifespan…
Local Universal Explainer (LUX) -- a rule-based explainer with factual, counterfactual and visual explanations
Szymon Bobek, Grzegorz J. Nalepa
Explainable artificial intelligence (XAI) is one of the most intensively developed area of AI in recent years. It is also one of the most fragmented with multiple methods that focu…
Explainable Predictive Maintenance
Sepideh Pashami, Slawomir Nowaczyk, Yuantao Fan +13
Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including…