Overview
Discover how to mitigate biases, apply AI harm frameworks to workplace scenarios, recognize security risks, and use AI responsibly with a practical checklist for effective implementation.
Syllabus
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- Introduction to Responsible AI
-- Overview of AI and its impact
-- Importance of responsible AI use
- Understanding and Mitigating Bias in AI
-- Types of biases in AI systems
-- Methods for identifying bias
-- Strategies to mitigate bias in AI models
- AI Harm Frameworks
-- Overview of common AI harm frameworks
-- Application of harm frameworks in workplace scenarios
-- Case studies of AI harm mitigation
- Recognizing AI Security Risks
-- Common security risks associated with AI
-- Strategies to address AI vulnerabilities
-- Best practices for AI system security
- Responsible AI Implementation Checklist
-- Key principles of responsible AI use
-- Practical checklist for AI implementation
-- Evaluating AI systems for ethical and responsible use
- Case Studies and Real-world Applications
-- Analysis of successful Responsible AI implementations
-- Lessons learned from AI misuse examples
- Conclusion and Future Directions
-- The evolving landscape of AI responsibility
-- Emerging trends and future challenges in AI ethics
Taught by
Tags
On-Demand
