WEBINAR "Trust What You Can Trace: Making Agentic AI Explainable"**Pre-registration is REQUIRED. RSVP here - [https://hubs.li/Q046VS--0](https://hubs.li/Q046VS--0)**
**Speaker:** Michael Novack, Solutions Architect at AIceberg
**Topic title:** "Trust What You Can Trace: Making Agentic AI Explainable, Secure, and Enterprise-Ready"
As enterprises adopt increasingly powerful AI systems, one critical challenge remains: understanding how and why these systems make decisions. Many modern models - especially large language models - operate as opaque “black boxes,” making transparency, compliance, and trust difficult to achieve.
*This webinar introduces the AI Explainability Scorecard, a practical framework for evaluating the transparency of AI systems based on criteria such as faithfulness, consistency, accessibility, and comprehensibility*. Attendees will learn how different model architectures - from K-Nearest Neighbors to neural networks and transformers - vary dramatically in explainability and risk.
**The session will also explore real-world techniques for improving visibility into complex models, including surrogate monitoring approaches that map AI behavior to comparable examples.** These methods help organizations build observable, auditable AI systems without sacrificing performance or scalability.
By the end of this session, attendees will understand how to move beyond black-box AI toward transparent, accountable, and secure AI deployment - a critical step for enterprises scaling agentic AI in high-stakes environments.
You will learn:
**\- Why Explainability Is the Foundation of Trustworthy AI**
*Learn why transparency isn’t just a technical preference - it’s becoming a legal, ethical, and operational requirement for enterprise AI systems.*
**\- The AI Explainability Scorecard Framework**
*Understand the five key criteria - faithfulness, comprehensibility, consistency, accessibility, and optimization clarity - for evaluating how explainable an AI model truly is.*
**\- How Different AI Models Compare in Transparency**
*Discover why some models are inherently interpretable while others require advanced methods to understand their behavior.*
**\- Practical Methods for Making Black\-Box AI Observable**
*Explore modern techniques - including surrogate monitoring models - that help organizations understand and audit large language models at scale.*
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