Abstract:
Machine Learning (ML) is increasingly prominent in organizations. While those algorithms can provide near perfect accuracy, their decision-making process remains opaque. In a context of accelerating regulation in Artificial Intelligence (AI) and deepening user awareness, explainability has become a priority notably in critical healthcare and financial environments. The various frameworks developed often overlook their integration into operational applications as discovered with our industrial partner. In this paper, explainability in ML and its relevance to our industrial partner is presented. We then discuss the main challenges to the integration of explainability frameworks in production we have faced. Finally, we provide recommendations given those challenges.
