All Episodes
Displaying 1 - 20 of 50 in total
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-or...
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 defi...
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 benefice...
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 ...
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 soci...
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...
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 wi...
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 ...
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, inc...
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...
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 thes...
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 qu...
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, ...
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, ...
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,...
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 bi...
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...
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...
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 fea...