{"type":"ScholarlyArticle","title":"Interpretable Local Concept-based Explanation with Human Feedback to Predict All-cause Mortality (Extended Abstract)","authors":[{"name":"Radwa El Shawi","slug":"radwa-el-shawi","given":"Radwa","family":"El Shawi","affiliation":"Institute of Computer Science , Tartu University , Estonia"},{"name":"Mouaz Al-Mallah","slug":"mouaz-al-mallah","given":"Mouaz","family":"Al-Mallah","affiliation":"Houston Methodist DeBakey Heart & Vascular Center Houston , TX , USA"}],"abstract":"Machine learning models are incorporated in different fields and disciplines, some of which require high accountability and transparency, for example, the healthcare sector. A widely used category of explanation techniques attempts to explain models' predictions by quantifying the importance score of each input feature. However, summarizing such scores to provide human-interpretable explanations is challenging. Another category of explanation techniques focuses on learning a domain representation in terms of high-level human-understandable concepts and then utilizing them to explain predictions. These explanations are hampered by how concepts are constructed, which is not intrinsically interpretable. To this end, we propose Concept-based Local Explanations with Feedback (CLEF), a novel local model agnostic explanation framework for learning a set of high-level transparent concept definitions in high-dimensional tabular data that uses clinician-labeled concepts rather than raw features.","doi":"10.24963/ijcai.2023/774","url":"https://ijcai.org/proceedings/2023/774","pdf":"https://ijcai.org/proceedings/2023/0774.pdf","markdown":"https://ijcai.org/proceedings/2023/774.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/774","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Journal Track","track":"Journal Track","number":774,"pages":{"first":6873,"last":6877},"keywords":["AI Ethics, Trust, Fairness: ETF: Trustworthy AI","AI Ethics, Trust, Fairness: General","AI Ethics, Trust, Fairness: ETF: Ethical, legal and societal issues","AI Ethics, Trust, Fairness: ETF: Explainability and Interpretability"],"videos":[],"references":[{"position":1,"raw":"[Al-Mallah and others, 2014] Mouaz H Al-Mallah et al. 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