Gender Bias in Machine Learning

Übersicht

Explore gender bias in machine learning with Shalvi Mahajan. Uncover AI's role in amplifying biases, real-world challenges, and evolving techniques to address them in product design and services across genders.

Lehrplan

• Introduction to Gender Bias in Machine Learning
Definition and scope
Historical context and significance
Overview of course structure and objectives
• Understanding Gender Bias in Data
Sources of gender bias in data collection
Case studies of biased datasets
Impact of biased data on AI outcomes
• How Machine Learning Amplifies Gender Bias
Mechanisms of bias perpetuation in algorithms
Analysis of bias in popular ML models
Real-world examples and consequences
• Identifying Bias in Machine Learning Systems
Techniques for detecting gender bias
Tools and metrics for measurement
Evaluating case studies for bias identification
• Addressing Gender Bias in AI Models
Strategies for bias mitigation
Fairness in model training and validation
Introduction to bias correction and adjustment methods
• Gender Bias in AI Applications
Case studies in different industries (e.g., healthcare, hiring, finance)
Ethical implications of biased AI applications
Lessons learned from industry failures and successes
• Designing Fair and Inclusive AI Products
Best practices for inclusive design
User-centric approaches for reducing bias
Stakeholder engagement and interdisciplinary collaboration
• Innovations and Evolving Techniques
Emerging research trends in gender bias mitigation
Technological advancements and their impact
Future directions and open challenges
• Conclusion and Future Directions
Recap of key learnings
Discussion on the evolving role of AI in gender equality
Paths for continued learning and advocacy
• Final Project
Project guidelines and expectations
Application of course concepts to a real-world problem
Presentation and peer review

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Fachgebiete

Data Science