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Course Outline

Introduction to Responsible AI and Ethics

  • Defining responsible AI and AI ethics.
  • The 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

  • Fundamental knowledge of AI and machine learning concepts.
  • Familiarity with data privacy and compliance standards.

Audience

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

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