AI Ethics and Bias Micro-Credential

AI Ethics and Bias Micro-Credential

Duration: 2 Hours

Description

AI systems don’t set out to be unfair — they inherit whatever bias is already sitting in their training data, and most teams don’t find out until a system is already in production and a decision is being questioned. This session walks through how that happens using real cases, gives participants hands-on practice auditing a dataset for hidden skew, and opens up the “black box” problem: why even the people who build a model often can’t fully explain a specific output. From there it moves into mitigation techniques, applied ethical frameworks, and human-in-the-loop review as a practical safeguard, not just a compliance checkbox.

Audience

This session is intentionally built for a mixed room — developers and data scientists who build or fine-tune models, and business leaders and non-technical staff who rely on AI-driven decisions without seeing how they’re made. Anyone responsible for signing off on an AI system, reviewing its outputs, or explaining a decision it influenced will get direct value from it, regardless of technical background.

Objectives

  • Identify different types of bias in AI systems
  • Understand the sources of bias, from data to design
  • Analyze the societal impact of biased AI on individuals and groups
  • Apply practical strategies to mitigate and address ethical challenges

Prerequisites

No prior technical knowledge is required, but a basic understanding of AI concepts is helpful.

Related AI Courses

See the full AI training roadmap and course directory for how this fits into a broader learning path.

For a deeper, organization-level treatment of AI risk, compliance, and policy, see AI Governance & Risk. New to generative AI concepts generally? Start with Beginner’s Guide to Generative AI Micro-Credential.

Course Outline

Module 1: The Problem of Bias

  • Bias in a Nutshell
  • Real-World Examples
  • The Data is the Key
  • Lab – Analyze a Dataset to Identify Potential Sources of Bias

Module 2: From Bias to Unfairness

  • Algorithmic Bias
  • The Black Box Problem
  • Fairness and Accountability
  • Lab – Discuss Ethical Dilemmas Relates to AI Systems

Module 3: Building a Responsible Future

  • Mitigation Strategies
  • Explainable AI
  • Ethical Frameworks
  • The Human in the Loop