{"type":"ScholarlyArticle","title":"BayCon: Model-agnostic Bayesian Counterfactual Generator","authors":[{"name":"Piotr Romashov","slug":"piotr-romashov","given":"Piotr","family":"Romashov","affiliation":"Università della Svizzera italiana , Switzerland"},{"name":"Martin Gjoreski","slug":"martin-gjoreski","given":"Martin","family":"Gjoreski","affiliation":"Università della Svizzera italiana , Switzerland"},{"name":"Kacper Sokol","slug":"kacper-sokol","given":"Kacper","family":"Sokol","affiliation":"RMIT University , Australia"},{"name":"Maria Vanina Martinez","slug":"maria-vanina-martinez","given":"María Vanina","family":"Martínez","affiliation":"Universidad de Buenos Aires , Argentina"},{"name":"Marc Langheinrich","slug":"marc-langheinrich","given":"Marc","family":"Langheinrich","affiliation":"Università della Svizzera italiana , Switzerland"}],"abstract":"Generating counterfactuals to discover hypothetical predictive scenarios is the de facto standard for explaining machine learning models and their predictions. However, building a counterfactual explainer that is time-efficient, scalable, and model-agnostic, in addition to being compatible with continuous and categorical attributes, remains an open challenge. To complicate matters even more, ensuring that the contrastive instances are optimised for feature sparsity, remain close to the explained instance, and are not drawn from outside of the data manifold, is far from trivial. To address this gap we propose BayCon: a novel counterfactual generator based on probabilistic feature sampling and Bayesian optimisation. Such an approach can combine multiple objectives by employing a surrogate model to guide the counterfactual search. We demonstrate the advantages of our method through a collection of experiments based on six real-life datasets representing three regression tasks and three classification tasks.","doi":"10.24963/ijcai.2022/104","url":"https://ijcai.org/proceedings/2022/104","pdf":"https://ijcai.org/proceedings/2022/0104.pdf","markdown":"https://ijcai.org/proceedings/2022/104.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/104","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"AI Ethics, Trust, Fairness","track":"Main Track","number":104,"pages":{"first":740,"last":746},"keywords":["Machine Learning: Hyperparameter Optimization","AI Ethics, Trust, Fairness: Trustworthy AI","Constraint Satisfaction and Optimization: Applications","AI Ethics, Trust, Fairness: Explainability and Interpretability"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986687","length_sec":88,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984830","length_sec":319,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Breunig et al., 2020] Markus M. 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