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AI evaluators comparing datasets metrics human judgements thresholds and uncertainty
SQA, Test Automation & SDET · Course #060

AI Quality Evaluation & Validation

In modern delivery environments, ai quality evaluation & validation must support fast feedback without sacrificing evidence or control. This course enables learners to design defensible AI evaluation programmes with clear constructs, datasets, metrics, human review and documented uncertainty, working through evaluation, verification and validation in the ai lifecycle, intended use, claims, risks and evaluation constructs and dataset sampling, coverage and challenge-case design. Learners translate intended use into measures, sample representative cases, compare evaluators, analyse subgroup results, establish thresholds and plan independent validation and change-triggered re-evaluation. Exercises require clear assumptions, useful failure messages and proportionate review. The course emphasises validity and repeatability so dashboards do not create false confidence from weak measures.

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Overview

In modern delivery environments, ai quality evaluation & validation must support fast feedback without sacrificing evidence or control. This course enables learners to design defensible AI evaluation programmes with clear constructs, datasets, metrics, human review and documented uncertainty, working through evaluation, verification and validation in the ai lifecycle, intended use, claims, risks and evaluation constructs and dataset sampling, coverage and challenge-case design. Learners translate intended use into measures, sample representative cases, compare evaluators, analyse subgroup results, establish thresholds and plan independent validation and change-triggered re-evaluation. Exercises require clear assumptions, useful failure messages and proportionate review. The course emphasises validity and repeatability so dashboards do not create false confidence from weak measures.

Course Highlights

Evaluation construct design

Representative and challenge datasets

Metric and evaluator reliability

Human-review protocols

Threshold and uncertainty decisions

Modules & Curriculum

Evaluation, verification and validation in the AI lifecycle

Intended use, claims, risks and evaluation constructs

Dataset sampling, coverage and challenge-case design

Metrics for performance, robustness, fairness and safety

Human evaluation: rubrics, calibration and agreement; automated evaluators and judge-model limitations

Thresholds, uncertainty and decision trade-offs

Reproducibility, versioning and experiment records

Independent validation and separation of duties; evaluation report and ongoing monitoring plan

Learning Outcomes

  • Translate intended use and risks into measurable evaluation constructs.
  • Design representative, edge and adversarial evaluation datasets.
  • Assess metric validity, reliability and known limitations.
  • Create human-evaluation rubrics and agreement checks.
  • Set thresholds with explicit trade-offs and uncertainty.

Ready to Build Your Future-Ready Skills?

Create AI evaluations that are valid, repeatable and useful for real decisions.

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