Beyond Quality: Measuring Trust in AI Outcomes

When evaluating the results of AI, traditional quality metrics no longer give a full picture. AI can deliver functionality and pass tests, yet still raise the question:

-- Can we trust the outcomes of AI?

As AI adoption grows, we enter complex, uncertain territory where quality metrics must be complemented by trust metrics.

This talk explores how to enhance traditional quality measures with trust-oriented metrics—across code generation and business applications—highlighting when trust metrics matter most and offering 2–3 proven approaches to quantify and apply them effectively.

Value for the audience:
Participants will learn how to complement traditional quality metrics with trust-oriented ones when evaluating AI. They will leave with clear examples and 2–3 practical methods to measure trust in AI outcomes, useful for both technical solutions and business applications.

Problems addressed:
Traditional quality metrics are not enough to evaluate the outcomes AI.

Talk language: English
Level: Expert
Target group: software architects, product managers, decision makers

Company:
BSC Designer

Alexis Savkin