Artificial Intelligence and Deep Learning
03/11/2024AI for Business Intelligence
03/11/2024AI Ethics and Governance
£4,500.00
Overview:
This course explores the essential ethical considerations and governance frameworks necessary for responsible AI development and deployment. With a focus on recent regulatory advancements and real-world applications, participants will delve into key topics such as bias mitigation, transparency, accountability, and compliance. By the end, they’ll be equipped to implement and monitor ethical AI practices across organizational functions.
Program Objectives:
At the end of this program, participants will be able to:
- Understand and apply core principles of AI ethics within regulatory frameworks.
- Conduct risk assessments and ethical audits for AI systems.
- Identify, mitigate, and monitor biases in AI models.
- Ensure transparency and explainability for various stakeholders.
- Establish accountability mechanisms to maintain responsible AI practices.
- Develop and implement comprehensive AI governance frameworks that comply with regulations.
Target Audience:
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- AI Developers, Data Scientists, and Engineers
- IT Managers, Directors, and AI Strategists
- Compliance and Risk Management Professionals
- Project Managers, Business Leaders, and Executives
- Policy Makers, Regulators, and AI Governance Officers
Program Outline:
Day 1: Foundations of AI Ethics and Societal Impact
- Introduction to AI Ethics – Importance and Relevance Across Roles.
- Core Principles of AI Ethics – Fairness, Accountability, Transparency.
- Ethical Decision-Making Frameworks.
- Societal Impact of AI Ethics – Real-World Case Studies.
- Hands-On Exercise: Analyzing Ethical Scenarios in AI Projects.
- Reflection & Review: Group Discussion on Ethical Challenges and Societal Impact.
Day 2: Identifying and Mitigating Bias in AI Models
- Sources and Types of Bias in AI Systems.
- Detecting and Quantifying Bias in Models.
- Mitigating Bias at Different Stages of AI Development.
- Tools and Frameworks for Bias Mitigation.
- Hands-On Exercise: Bias Detection and Mitigation in Sample AI Models.
- Reflection & Review: Evaluating Bias Mitigation Strategies.
Day 3: Enhancing Transparency, Explainability, and Accountability
- Importance of Transparency and Explainability for AI Models.
- Techniques for Explainable AI (XAI) and Visualizing Decision Processes.
- Designing Accountability Standards and Mechanisms.
- Legal and Regulatory Responsibilities.
- Hands-On Exercise: Creating a Transparency and Accountability Framework.
- Reflection & Review: Best Practices for Transparent and Accountable AI.
Day 4: Data Privacy, Security, and AI Governance
- Data Privacy Principles in AI Applications.
- Topic Securing AI Systems Against Data Breaches.
- Developing AI Governance Policies.
- Compliance Standards (e.g., GDPR, EU AI Act).
- Hands-On Exercise: Designing a Governance Plan for a Sample AI Project.
- Reflection & Review: Discussion on Implementing Data Privacy and Security.
Day 5: Capstone Project and Future Directions in AI Ethics
- Capstone Project: Developing an AI Ethics Policy for a Business Scenario.
- Future Trends in AI Ethics and Governance.
- Reflection & Review: Capstone Project Presentations and Final Q&A.