The Myth of Neutrality - How AI is Widening Social Divides

Übersicht

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.

Lehrplan

• 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

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Fachgebiete

Conference Talks