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Responsible AI team reviewing subgroup impacts explanations safety and human oversight
SQA, Test Automation & SDET · Course #062

Responsible AI Testing

Reliable quality work depends on more than knowing a tool name. In Responsible AI Testing, participants learn to evaluate AI systems for trustworthiness, fairness, transparency, safety, privacy and accountable human oversight, using trustworthy and responsible ai quality characteristics and intended use, stakeholders, impacts and risk tolerance as practical anchors. Learners map affected stakeholders, define impact-oriented criteria, test subgroup behaviour and explanations, inspect human control paths and document evidence using risk functions such as govern, map, measure and manage. The programme also examines governance roles, accountability and evidence ownership so that decisions remain maintainable, observable and useful to the wider delivery team. The course turns responsible-AI principles into testable claims and ongoing controls without presenting a generic ethics checklist as assurance.

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Overview

Reliable quality work depends on more than knowing a tool name. In Responsible AI Testing, participants learn to evaluate AI systems for trustworthiness, fairness, transparency, safety, privacy and accountable human oversight, using trustworthy and responsible ai quality characteristics and intended use, stakeholders, impacts and risk tolerance as practical anchors. Learners map affected stakeholders, define impact-oriented criteria, test subgroup behaviour and explanations, inspect human control paths and document evidence using risk functions such as govern, map, measure and manage. The programme also examines governance roles, accountability and evidence ownership so that decisions remain maintainable, observable and useful to the wider delivery team. The course turns responsible-AI principles into testable claims and ongoing controls without presenting a generic ethics checklist as assurance.

Course Highlights

Impact-oriented test design

Fairness and subgroup evaluation

Transparency and explanation checks

Safety and robustness evidence

Human oversight and contestability

Modules & Curriculum

Trustworthy and responsible AI quality characteristics

Intended use, stakeholders, impacts and risk tolerance

Governance roles, accountability and evidence ownership

Fairness concepts, subgroup analysis and measurement limits

Transparency, explainability and user communication

Safety, robustness and foreseeable misuse

Privacy, data governance and consent-related checks

Human oversight, override and contestability; incident, monitoring and change-triggered reassessment

Responsible AI test plan and evidence review

Learning Outcomes

  • Identify affected stakeholders, benefits, harms and accountability boundaries.
  • Translate responsible-AI principles into testable requirements.
  • Evaluate subgroup performance and fairness trade-offs.
  • Assess transparency, explanations and user-facing limitations.
  • Verify human review, override, appeal and incident controls.

Ready to Build Your Future-Ready Skills?

Turn responsible-AI commitments into testable criteria, evidence and improvement actions.

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