In the rapidly evolving landscape of identity and access management (IAM), the integration of Artificial Intelligence (AI) brings forth both unprecedented opportunities and significant challenges. This talk examines the critical importance of trust and transparency in AI systems, particularly in the context of IAM. We explore how AI decisions are made and communicated, emphasizing the need for Explainable AI (XAI) to demystify complex processes and foster user confidence.
We begin by exploring diverse real-world scenarios where AI's pivotal role in access management is demonstrated. This includes examining how AI systems are employed to make automated access decisions, while highlighting the challenges and successes in building trust through transparency. Detailed attention is given to XAI models and techniques such as LIME and SHAP, which are used to make AI's decision-making process accessible and understandable to all users.
The talk then transitions to innovative ways XAI can enhance user experience in IAM systems. We discuss how XAI not only clarifies AI decisions for users but also contributes to creating more intuitive, personalized, and user-friendly interfaces. Through examples and case studies, we showcase how XAI has been instrumental in reducing user frustration, increasing system adoption, and improving overall satisfaction.
Attendees will gain insights into the practical applications and benefits of transparent AI systems in identity governance, the significance of XAI in bridging the gap between complex AI algorithms and user-centric experiences, and how these elements collectively contribute to more secure, efficient, and trustworthy IAM solutions.
