Overview
Explores how AI systems perpetuate and amplify societal biases, examining real-world examples of algorithmic discrimination and discussing potential solutions for building more equitable AI technologies.
Syllabus
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- Introduction to AI and Bias
-- Overview of AI technologies and their societal impact
-- Definition and types of biases in AI systems
- Historical Context of AI Bias
-- Evolution of AI and its societal role
-- Notable incidents of AI-related discrimination
- Mechanisms of Bias in AI
-- Data bias and its origins
-- Algorithmic bias and decision-making processes
-- Feedback loops and bias amplification
- Case Studies of Algorithmic Discrimination
-- Facial recognition and racial profiling
-- Bias in hiring algorithms
-- Disparities in healthcare AI
- Societal Impact of AI-Driven Bias
-- Marginalization of communities
-- Economic and social disparities
-- Legal and ethical considerations
- Frameworks for Analyzing AI Bias
-- Cross-disciplinary approaches to studying bias
-- Intersectional analysis of bias in AI systems
- Approaches to Mitigating Bias in AI
-- Data collection and preprocessing techniques
-- Algorithm design and fairness constraints
-- Post-deployment monitoring and auditing
- Building Equitable AI Technologies
-- Community-involved AI design
-- Policy and regulation for equitable AI
-- Collaborative efforts between technologists, policymakers, and communities
- Future Directions and Challenges
-- Emerging technologies and new forms of bias
-- Long-term strategies for AI fairness
- Conclusion
-- Summary of key insights
-- Recommendations for stakeholders in AI development
- Additional Resources
-- Recommended readings and case studies
-- Online forums and communities for ongoing discussions
Taught by
Tags
On-Demand
