Transparent AI: The Case for Interpretability and Explainability
It tackles the problem of ensuring responsible and trustworthy AI for organizations implementing AI in critical sectors, but it is incremental as it builds on existing interpretability concepts without introducing new methods.
The paper addresses the need for transparency in AI systems for high-stakes decisions by providing actionable strategies and implementation guidance to integrate interpretability as a core design principle, based on insights from practical applications across diverse domains.
As artificial intelligence systems increasingly inform high-stakes decisions across sectors, transparency has become foundational to responsible and trustworthy AI implementation. Leveraging our role as a leading institute in advancing AI research and enabling industry adoption, we present key insights and lessons learned from practical interpretability applications across diverse domains. This paper offers actionable strategies and implementation guidance tailored to organizations at varying stages of AI maturity, emphasizing the integration of interpretability as a core design principle rather than a retrospective add-on.