Module 1 : Foundations of AI Ethics & Societal Impact

● Understanding Ethical Context : Explore why ethical AI matters for organizations, society and individuals.

● AI’s Societal Effects : Examine both the positive and negative impacts of AI adoption across industries.

● Impact Assessment Skills : Learn how to conduct lightweight ethical and social impact scans before deploying AI systems.

Module 2 : Bias, Fairness & Equity in AI

● Sources of Bias : Understand how flawed data, model assumptions and system design introduce bias.

● Fairness Strategies : Learn practical mitigation methods to reduce disparate impact.

● Hands On Bias Detection : Use AI Fairness 360 to identify and analyze bias in an example dataset.

Module 3 : Transparency, Explainability & Stakeholder Trust

● Importance of Explainable AI : Understand why transparent model behavior is critical for decision making and compliance.

● Explainability Techniques : Practice using AI Explainability 360 to generate interpretable model insights.

● Communicating AI Decisions : Learn to tailor explanations for regulators, executives, customers and technical teams.

Module 4 : Privacy, Security & Safe AI Deployment

● Privacy First Design : Learn principles such as data minimization, anonymization and safe data handling.

● Security & Safety Controls : Understand guardrails including monitoring, red‑teaming and input/output filtering.

● Responsible GenAI Use : Review risks related to hallucinations, prompt injection and unauthorized data exposure.

Module 5 : Accountability & Responsible AI Ownership

● Defining Accountability : Learn how roles and responsibilities are assigned across AI lifecycle stakeholders.

● Organizational Responsibilities : Explore what obligations developers, leaders and oversight teams carry.

● Documentation Practices : Understand model cards, decision logs and other accountability‑driven documentation tools.

Module 6 : Legal, Regulatory & Compliance Requirements

● Global Regulatory Landscape : Overview of key AI related regulations and compliance obligations.

● Legal Risks & Case Studies : Examine GDPR linked examples and real world compliance failures.

● Compliance Mapping : Learn how to evaluate an AI use case against regulatory and organizational requirements.

Module 7 : Ethical Decision‑Making & Structured Evaluation

● Decision Making Frameworks : Use structured models to weigh benefits, risks and unintended consequences.

● Scenario Based Practice : Apply ethical frameworks to realistic business dilemmas.

● Simulation Exercises : Work through complex situations requiring trade offs and rapid ethical judgment.

Module 8 : AI Governance, Policies & Global Ethics Standards

● Governance Principles : Understand the building blocks of responsible AI oversight and risk tiering.

● Operationalizing Best Practices : Develop policy guidelines and workflow controls for real implementation.

● Using Global Standards : Review IEEE, EU and international ethics standards and learn how to benchmark AI systems.