How do you turn "be ethical" into something an organization can actually manage? In this episode of Checks & Algorithms, we explore the ethical pillars, risk taxonomies, and technical safeguards behind Responsible AI (RAI), bridging high-level principles and day-to-day risk management. It's built for boot camp candidates preparing for the IAPP AIGP exam, and useful for anyone responsible for making AI trustworthy in practice.

**In this episode:**

- **The four levels of AI harm:** Individual harms (surveillance, unfair credit or housing decisions, privacy breaches, physical and psychological injury), group harms (algorithmic bias, disparate impact, profiling), societal harms (deepfakes and disinformation, threats to democratic processes, environmental cost of compute, labor disruption), and corporate and ecosystem risks (reputational damage, IP liability, prompt injection and data poisoning, regulatory fines, supply chain dependencies).

- **Six traits of trustworthy AI:** Accountability and governance; transparency and notice; explainability vs. interpretability, and why explanations matter for adverse action notices in credit and employment; safety, robustness, and reliability against adversarial attacks; privacy and data governance; and fairness and non-discrimination across the AI lifecycle.

- **Privacy-enhancing technologies (PETs):** How federated learning, synthetic data, differential privacy, and homomorphic encryption let teams train and use models while protecting personal data.

- **Global ethical frameworks compared:** The OECD AI Principles, the White House Blueprint for an AI Bill of Rights, the EU High-Level Expert Group's seven requirements and its Assessment List for Trustworthy AI (ALTAI), the UNESCO Recommendation on the Ethics of AI, the IEEE 7000 series, and the Fair Information Practice Principles (FIPPs).

**Who should listen:**

- **AIGP candidates** covering Domain II: AI harms, trustworthy AI principles, and ethical frameworks.

- **Risk management and compliance officers** putting risk taxonomies and PETs to work inside enterprise risk programs.

- **Legal counsel and privacy officers** who want a structured look at explainability requirements, adverse action notices, and global policy frameworks.

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Disclaimer: AIGP official source information + Gemini + Human Reviewer