2021/04/01 by Gabriel Ferrettini, Elodie Escriva, Julien Aligon +2
Computer Science · Social Sciences · #Adversarial Robustness in Machine Learning #Black box #Computation #Efficient algorithm #Ethics and Social Impacts of AI #Explainable Artificial Intelligence (XAI) #Predictive modelling #Variety (cybernetics) #cs.LG
paper · pdf · doi:10.1007/s10796-021-10141-9
published as Information Systems Frontiers (2021) · Paper submitted to Information Systems Frontiers (Special Issue of the ADBIS 2020 conference)
arxiv created 2021/04/01 · openalex created_date 2021/04/13 · openalex publication_date 2021/05/22 · arxiv updated 2021/05/25 · openalex updated_date 2026/08/05
As Machine Learning (ML) is now widely applied in many domains, in both research and industry, an understanding of what is happening inside the black box is becoming a growing demand, especially by non-experts of these models. Several approaches had thus been developed to provide clear insights of a model prediction for a particular observation but at the cost of long computation time or restrictive hypothesis that does not fully take into account interaction between attributes. This paper provides methods based on the detection of relevant groups of attributes -- named coalitions -- influencing a prediction and compares them with the literature. Our results show that these coalitional methods are more efficient than existing ones such as SHapley Additive exPlanation (SHAP). Computation time is shortened while preserving an acceptable accuracy of individual prediction explanations. Therefore, this enables wider practical use of explanation methods to increase trust between developed ML models, end-users, and whoever impacted by any decision where these models played a role.