{"type":"ScholarlyArticle","title":"Visual Analytics for Guiding Feature Attribution Method Selection","authors":[{"name":"Priscylla Silva","slug":"priscylla-silva","given":"Priscylla","family":"Silva","affiliation":""}],"abstract":"Feature attribution methods explain model predictions by assigning importance to input features. Choosing the most suitable method is difficult due to inconsistencies and a lack of universal solutions. Our research focuses on proposing tools and methodologies for evaluating, comparing, and selecting these methods.","doi":"10.24963/ijcai.2025/1243","url":"https://ijcai.org/proceedings/2025/1243","pdf":"https://ijcai.org/proceedings/2025/1243.pdf","markdown":"https://ijcai.org/proceedings/2025/1243.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1243","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":1243,"pages":{"first":10977,"last":10978},"keywords":["AI Ethics, Trust, Fairness: ETF: Explainability and interpretability","AI Ethics, Trust, Fairness: ETF: Trustworthy AI"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}