Interpretable Local Concept-based Explanation with Human Feedback to Predict All-cause Mortality (Extended Abstract)

Interpretable Local Concept-based Explanation with Human Feedback to Predict All-cause Mortality (Extended Abstract)

Radwa El Shawi, Mouaz Al-Mallah

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
Journal Track. Pages 6873-6877. https://doi.org/10.24963/ijcai.2023/774

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.
Keywords:
AI Ethics, Trust, Fairness: ETF: Explainability and Interpretability
AI Ethics, Trust, Fairness: ETF: Trustworthy AI
AI Ethics, Trust, Fairness: ETF: Ethical, legal and societal issues
AI Ethics, Trust, Fairness: General