You will move from judging AI output to systematically stress-testing it. Run red-team bias protocols, detect hallucinations across many outputs, and assess whether images, audio, or video are synthetic, producing failure-mode reports, accuracy scorecards, and authenticity reports along the way. Verification builds the confidence needed to put AI-assisted work in front of stakeholders.

Building on core AI-evaluation skills, this module shows how to use generative AI responsibly and ethically at work. Using ChatGPT and provided workplace scenarios, you will reason through privacy, disclosure, bias, and accountability questions and draft a personal responsible-use checklist. Getting the ethics right is what makes AI-augmented work trustworthy to colleagues, customers, and regulators.

Calibrate how much to trust an AI output and when to override it. Score outputs with a confidence rubric, catalog uncertainty signals that mark tool limits, and make documented trust/override decisions across varied scenarios. Calibrated reliance builds the foundational judgment required to keep AI-augmented decisions safe.

In this capstone-style project you assemble a single, portfolio-ready decision justification package for a realistic high-stakes decision, integrating the full set of critical AI decision-making skills. Using ChatGPT and provided templates, you will verify AI outputs, calibrate confidence, run root cause analysis, and produce a transparent, stakeholder-facing justification. It is the artifact that shows a hiring manager you can lead AI-augmented decisions responsibly.