An AI with an Agenda - How Our Cognitive Biases Leak Into Machine Learning

Overview

Explores how human cognitive biases can infiltrate AI and machine learning models, offering strategies to mitigate these issues and create more objective, ethical algorithms.

Syllabus

• Introduction to Cognitive Biases
Definition and Overview
Common Cognitive Biases and Their Impact
• Basics of Machine Learning
Overview of Machine Learning Models
Data Collection and Feature Engineering
• Cognitive Biases in AI
How Biases Enter AI Systems
Case Studies of Bias in AI
• Identifying Biases in Data and Models
Techniques for Bias Detection
Tools for Bias Measurement
• Mitigation Strategies
Data Preprocessing Techniques
Algorithmic Adjustments and Fairness Constraints
• Ethics and AI
The Importance of Ethical AI
Stakeholder Perspectives and Social Implications
• Case Studies and Applications
Examples from Various Industries
Analysis of Bias Mitigation Outcomes
• Future Directions in Bias Mitigation
Emerging Technologies and Methods
Ongoing Challenges and Research Areas
• Course Review and Conclusion
Recap of Key Concepts
Final Thoughts on Ethical AI Development

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Subjects

Conference Talks