RAI Transparency & Explainability Best Practices
This workshop helps leaders translate Responsible AI transparency and explainability into practical expectations that teams can apply across real GenAI use cases. You’ll clarify what “good” looks like at the system level, identify common explainability gaps that create trust and compliance risk, and define how to communicate explanations that are accurate, understandable, and appropriate for different stakeholders.
Leave with a clear understanding of transparency and explainability best practices—and a prioritized set of next steps to strengthen them across your GenAI initiatives.
As GenAI scales, organizations often lack consistent standards for explaining outcomes in ways stakeholders can trust and audit.
- Explainability expectations are unclear: Leaders and teams don’t share a consistent definition of what must be explainable, to whom, and why.
- Stakeholder trust breaks down: Without credible explanations, customers, regulators, and internal stakeholders question decisions and outcomes.
- Validation doesn’t include explainability: Many review processes focus on “does it work,” not “can we explain it” in a defensible way.
We align leaders on practical best practices for transparency and explainability—and how to translate them into decision-ready guardrails.
- Transparency and explainability principles: Establish clear leadership expectations for what must be understandable, traceable, and reviewable.
- System-level gap identification: Pinpoint where GenAI experiences lack clarity (inputs, outputs, and decision influence) and why it matters.
- Explainability methods—made usable: Understand the range of explainability approaches and when each is appropriate based on use case risk.
- Stakeholder-ready explanations: Define how to communicate explanations that balance simplicity, completeness, and defensibility for each audience.
- Governance and validation integration: Embed explainability expectations into reviews, approvals, and ongoing monitoring so they hold over time.
- Define AI transparency and explainability principles
- Assess system-level explainability gaps in GenAI models
- Leverage techniques like SHAP, LIME, and causal inference for explaining AI outcomes
- Develop user-facing explanations that balance clarity and accuracy
- Integrate explainability into model validation and regulatory compliance
Establish a shared definition of transparency and explainability requirements by stakeholder (internal and external)
Prioritize a view of the most material explainability gaps across your current or planned GenAI use cases
Set clear standards for user-facing explanations, including what must be disclosed and how to communicate uncertainty responsibly
Apply a practical approach to incorporating explainability into review, approval, and monitoring routines
Define a set of prioritized next steps to strengthen transparency and explainability in a way that improves trust and defensibility
Who Should Attend:
Solution Essentials
Facilitated workshop (in-person or virtual)
4 hours
Intermediate
Shared collaboration space (virtual whiteboard or equivalent) and shared notes