Understanding role of Ethics in managing AI in modern business #254007

Upon successful completion of this course, participants will be able to:

• Articulate the core ethical principles of AI (e.g., fairness, accountability, transparency) and their critical importance to business risk and brand trust.

• Identify and assess potential ethical risks and unintended consequences throughout the AI project lifecycle, from data collection to deployment.

• Apply practical frameworks and tools (e.g., fairness metrics, impact assessments, model cards) to evaluate and mitigate bias and other harms in AI systems.

• Develop the key components of an AI Ethics Governance program, including policies, review boards, and audit trails.

• Navigate the evolving global regulatory landscape for AI (e.g., EU AI Act, US Executive Order).

• Communicate effectively with technical teams, executives, and the public about AI ethics to build transparency and trust.

This course is essential for any professional involved in the oversight, development, or deployment of AI systems within an organization.

• Senior Executives & C-Suite Leaders (CEOs, COOs, CTOs, CIOs)

• Heads of Legal, Risk, Compliance, and Governance

• Product Managers & Product Owners of AI-powered products

• Data Scientists & ML Engineers who build models

• HR Directors using AI for hiring and talent management

• Marketing Leaders leveraging AI for customer engagement

• Corporate Social Responsibility (CSR) Officers

• Board Members providing oversight on technology risk

Prerequisites: No technical expertise is required. An understanding of how AI is used in business processes is helpful.

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: The Business Case for Ethical AI

• Module 1: Why Ethics is a Core Business Imperative, Not a Niche Concern

  • The high cost of unethical AI: Case studies of failures (bias in hiring, discriminatory algorithms, privacy breaches).

  • The positive ROI of trust: How ethical AI enhances brand reputation, customer loyalty, and employee satisfaction.

• Module 2: Foundational Principles of AI Ethics

  • Deep dive into fairness, accountability, transparency (Explainability), privacy, and safety.

  • Exploring the tensions between principles (e.g., privacy vs. transparency).

• Workshop: Analyzing real-world AI failures through an ethical lens to identify root causes.

Day 2: Identifying and Mitigating Bias in AI Systems

• Module 3: The Many Faces of Bias

  • How bias creeps in: historical data, model design, and human feedback loops.

  • Types of bias: statistical, representation, and measurement bias.

• Module 4: Technical and Process Solutions for Fairness

  • Introduction to fairness metrics and bias detection tools (e.g., AI Fairness 360).

  • Processes for debiasing: pre-processing, in-processing, and post-processing techniques.

• Hands-On Session: Using a software tool to audit a sample dataset and model for biased outcomes.

Day 3: Transparency, Explainability, and Human Oversight

• Module 5: The “Black Box” Problem

  • The need for explainability: regulatory compliance, user trust, and model debugging.

  • Techniques for Explainable AI (XAI): LIME, SHAP, and counterfactual explanations.

• Module 6: Designing for Human-in-the-Loop

  • The role of human judgment in automated systems.

  • Designing UIs that present AI recommendations and their rationale effectively.

• Case Study: How a financial institution uses XAI to explain credit denial decisions to comply with regulations.

Day 4: Building Governance and Navigating Regulation

• Module 7: Implementing AI Ethics Governance

  • Creating an AI Ethics Charter or Policy.

  • Structuring an AI Ethics Review Board: mandate, composition, and processes.

• Module 8: The Global Regulatory Landscape

  • Overview of key regulations: EU AI Act (risk-based approach), US Executive Order, and others.

  • Conducting an AI Risk Assessment and implementing conformity measures.

• Workshop: Role-playing an ethics review board meeting to evaluate a proposed AI project.

Day 5: From Theory to Practice: Leading an Ethical AI Culture

• Module 9: The Ethics Lifecycle: Integrating Ethics from Concept to Deployment

  • Embedding ethics into the AI project lifecycle: checklists, documentation (e.g., Model Cards, Datasheets), and continuous monitoring.

• Module 10: Communicating and Leading on AI Ethics

  • How to talk about AI ethics with different stakeholders: engineers, executives, and customers.

  • Creating a training program to upskill your organization.

• Capstone Project: Participants develop a comprehensive Responsible AI strategy and action plan for their own organization or a case study, covering policy, governance, tools, and communication.

• Conclusion: Committing to Action – Developing a personal pledge and first steps for implementation.