Course Outline

Introduction to Responsible AI and Ethics

  • Defining responsible AI and AI ethics
  • Importance of ethical considerations in AI applications
  • Key principles: fairness, accountability, transparency

Bias in AI and Mitigation Strategies

  • Understanding bias in AI models and data
  • Types of biases and their impacts on AI outcomes
  • Bias mitigation techniques: pre-processing, in-processing, and post-processing

Ethical Auditing and Accountability in AI

  • Introduction to AI auditing frameworks and tools
  • Conducting audits to assess fairness and transparency
  • Implementing accountability measures in AI systems

Exploring Ethical Frameworks and Compliance

  • Overview of ethical frameworks like the EU AI Act and IEEE standards
  • Legal and regulatory compliance in AI systems
  • Case studies on responsible AI regulations and industry standards

Building Transparency and Explainability in AI

  • Introduction to explainable AI techniques
  • Building interpretable models for greater transparency
  • Using tools for model explainability and decision traceability

Governance and Risk Management in AI

  • Developing governance frameworks for responsible AI
  • Risk management and ethical considerations in AI deployment
  • Strategies for stakeholder engagement and oversight

Future Directions in Ethical AI

  • Emerging trends and challenges in AI ethics
  • Adapting governance frameworks for future AI technologies
  • Promoting an ethical AI culture within organizations

Summary and Next Steps

Requirements

  • Basic understanding of AI and machine learning concepts
  • Familiarity with data privacy and compliance standards

Audience

  • Data scientists and AI practitioners interested in ethical AI development
  • Compliance officers and legal professionals overseeing AI regulation
  • Business leaders and decision-makers involved in AI strategy and governance
 14 Hours

Number of participants


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