AI Governance and Compliance
Ensure Responsible AI: Navigate Governance, Compliance, and Ethics in AI Systems
About This Course
Participants will explore AI governance models, regulatory landscapes, and strategies for ensuring compliance with global standards such as GDPR, HIPAA, and emerging AI-specific laws. The program also delves into ethical decision-making, AI bias mitigation, and building transparent, accountable AI systems that align with legal and societal expectations.
Aim
This program provides a comprehensive understanding of the frameworks, regulations, and best practices for governing AI technologies and ensuring compliance with ethical standards, data protection laws, and AI accountability.
Workshop Objectives
- Understand AI governance frameworks and their global implications.
- Learn about compliance requirements for AI systems.
- Explore ethical AI design and decision-making processes.
- Develop strategies for bias mitigation and fairness in AI systems.
- Build transparent, compliant, and accountable AI products.
Workshop Structure
Modules for AI Governance and Compliance:
- Introduction to AI Governance and Compliance
- What is AI Governance?
- The Importance of AI Governance and Compliance
- Key Regulatory Bodies and Standards (GDPR, CCPA, ISO, etc.)
- Ethics in AI Development and Deployment
- Ethical Principles in AI (Fairness, Accountability, Transparency, and Ethics – FATE)
- Case Studies on AI Failures and Ethical Challenges
- Bias, Discrimination, and Fairness in AI Models
- AI Compliance Frameworks and Regulations
- Overview of Global AI Regulations (GDPR, EU AI Act, CCPA)
- Key Components of an AI Compliance Framework
- Compliance Challenges in AI Model Development
- Risk Management for AI Systems
- Identifying and Mitigating Risks in AI Development and Deployment
- Managing Data Privacy and Security in AI Systems
- Tools and Techniques for Risk Assessment and Mitigation
- Bias and Fairness in AI Models
- Understanding and Identifying Bias in AI Algorithms
- Techniques for Auditing AI Models for Fairness
- Best Practices for Fairness in Data Collection, Model Training, and Deployment
- Transparency and Explainability in AI
- The Importance of Explainable AI (XAI)
- Tools and Techniques for Making AI Models Interpretable
- Regulatory Requirements for AI Transparency and Explainability
- AI Accountability and Responsibility
- Assigning Responsibility in AI Development
- AI Decision-Making: Human-in-the-Loop vs. Fully Automated Systems
- Accountability Frameworks for AI Systems
- Privacy and Data Protection in AI
- Understanding Data Privacy Regulations (GDPR, HIPAA, etc.)
- Techniques for Privacy-Preserving AI (Differential Privacy, Federated Learning)
- Managing Personally Identifiable Information (PII) in AI Systems
- Auditing and Monitoring AI Systems
- AI Model Auditing Practices
- Continuous Monitoring of AI Models for Compliance
- AI Model Lifecycle Management and Updates for Compliance
- AI Governance in Practice
- Building Governance Teams and AI Ethics Boards
- Best Practices for AI Governance Implementation in Organizations
- Creating AI Policies and Guidelines for Businesses
- Regulatory Challenges and Future of AI Governance
- Emerging Trends in AI Regulation
- Navigating Regulatory Changes and Preparing for the Future
- Case Studies on AI Regulatory Compliance
Who Should Enrol?
Data scientists, AI professionals, legal experts, compliance officers, and project managers working with AI systems.
Workshop Outcomes
- Mastery in navigating AI governance and compliance challenges.
- Proficiency in building fair, ethical, and transparent AI systems.
- Skills in managing AI risk and ensuring compliance with regulations.
- Hands-on knowledge of data privacy, bias mitigation, and AI accountability.
Fee Structure
Discounted: ₹8,499 | $112
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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