TY - GEN
T1 - Interpretable Local Concept-based Explanation with Human Feedback to Predict All-cause Mortality (Extended Abstract)
AU - Shawi, Radwa El
AU - Al-Mallah, Mouaz
N1 - Publisher Copyright:
© 2023 International Joint Conferences on Artificial Intelligence. All rights reserved.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85170393247&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85170393247&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85170393247
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 6873
EP - 6877
BT - Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
A2 - Elkind, Edith
PB - International Joint Conferences on Artificial Intelligence
T2 - 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
Y2 - 19 August 2023 through 25 August 2023
ER -