Check AI quality before release and map AI policies to risk frameworks
Also listed as: Model explainability & compliance
What is it?
Xelence plans to let your team score an AI feature against a set of expected results before it is released. It also plans to link AI policies to recognized AI risk frameworks, so reviewers can see which controls each policy supports. Both build on the record of every AI run in Xelence 10.0.
Why does it matter?
Public programs must show that AI features are fit for purpose before use, and that their AI controls match recognized standards. Without this, AI is hard to defend to auditors and oversight bodies.
Evidence that each AI feature met a quality bar before it went live, and easier compliance reviews.
Generative AI Services and Xelence AI Engine work together. Building AI and running it explains which does what.
Getting it into your solution
- Needs an upgrade to the Xelence release
- Needs configuration
- Changes how your team works
Where people stay in control
People set the expected results and the quality bar, decide whether a feature may be released, and remain responsible for decisions that affect constituents.
Start thinking about
- Which AI-related laws, policies or risk frameworks apply to your programs?
- Who in your organization would set the quality bar for an AI feature and approve its release?
- What examples of correct answers could your staff provide to test an AI feature?
Terms used on this card
- Large language model (LLM)
- The kind of AI model that understands and writes text, used behind AI assistants and agents.
- Tracing
- Recording each step a request takes through the system, so support teams can see exactly where something slowed down or failed.
Items shown as planned are subject to change based on customer priorities for specific features and enhancements.