Certified - Responsible AI Audio Course

Episode 20 — Model, Data & System Cards

Episode 19 — Explainer Tooling

Explainer tools operationalize post hoc explainability by generating insights into model behavior. This episode introduces SHAP, which uses game theory to allocate featur

Episode 18 — Interpretable Models vs. Post hoc Explanations

This episode contrasts two approaches to explainability: inherently interpretable models and post hoc explanation methods. Interpretable models, such as decision trees an

Episode 17 — Why Explainability?

Explainability refers to making AI outputs understandable to humans, a necessity for trust, compliance, and accountability. This episode explains why explainability is di

Episode 16 — Mitigating Bias

Measuring bias is only the first step; mitigation strategies are required to reduce unfair outcomes in AI systems. This episode introduces three broad categories of bias

Episode 15 — Measuring Bias

Once fairness definitions are understood, the next step is measuring bias within data and models. This episode explains how metrics quantify disparities across groups, us

Episode 14 — Fairness Definitions

Fairness in AI does not have a single definition but instead encompasses multiple, sometimes conflicting, interpretations. This episode introduces demographic parity, whi

Episode 13 — Documenting Data

Documenting datasets is critical for transparency, accountability, and reproducibility in AI systems. This episode introduces methods such as datasheets for datasets, dat

Episode 12 — Data Governance 101

Data governance establishes the rules and responsibilities for managing the information that powers AI systems. This episode defines data governance as encompassing quali

Episode 11 — Internal AI Policies & Guardrails

Internal AI policies provide organizations with concrete rules for developing, deploying, and using artificial intelligence responsibly. This episode explains how these p

Episode 10 — AI Management Systems

An AI management system refers to organizational structures and processes that operationalize responsible AI. This episode explains how such systems mirror established mo

Episode 9 — Risk Management Frameworks

Structured frameworks provide organizations with consistent methods for identifying, assessing, and mitigating AI risks. This episode introduces well-known models, includ

Episode 8 — AI Regulation in Practice

AI regulation increasingly applies a risk-tiered framework, where obligations scale with the potential for harm. This episode explains how regulators classify systems int

Episode 7 — Policy Basics for Non Lawyers

Artificial intelligence systems do not exist outside the scope of established laws. This episode introduces policy areas most relevant to AI, ensuring that learners witho

Episode 6 — The Responsible AI Lifecycle

Responsible AI requires integration across every stage of the AI lifecycle rather than relying on after-the-fact corrections. This episode introduces a structured view of

Episode 5 — Stakeholders and Affected Communities

AI systems affect not only direct users but also a wide range of stakeholders, from secondary groups indirectly influenced by decisions to broader communities and societi

Episode 4 — The AI Risk Landscape

Artificial intelligence introduces a wide spectrum of risks, ranging from technical failures in models to ethical and societal harms. This episode maps the categories of

Episode 3 — Guiding Principles in Plain Language

This episode translates the most common responsible AI principles into accessible language for both technical and non-technical audiences. Core values include beneficence

Episode 2 — What “Responsible AI” Means—and Why It Matters

Responsible AI refers to building and deploying artificial intelligence systems in ways that are ethical, trustworthy, and aligned with human values. This episode defines

Episode 1 — Welcome & How to Use This PrepCast

This opening episode introduces the structure and intent of the Responsible AI PrepCast. Unlike certification-focused courses, this series is designed as a practice-orien