Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values

Overview

Explore how decision tree algorithms can achieve fair machine learning predictions when dealing with missing data values, without requiring traditional data imputation methods.

Syllabus

• Introduction to Fairness in Machine Learning
Understanding Fairness in Statistical Models
Overview of Bias and Ethical Implications
• The Challenge of Missing Data in Machine Learning
Types and Causes of Missing Data
Consequences of Missing Data on Model Accuracy and Fairness
• Decision Trees: An Overview
Basic Structure and Algorithms
Advantages and Limitations in Handling Missing Data
• Fairness Criteria and Measurements
Definitions and Types of Fairness (e.g., Demographic Parity, Equalized Odds)
Measuring Fairness in Statistical Models
• Traditional Approaches to Handling Missing Data
Data Imputation Methods
Limitations of Imputation in Preserving Fairness
• Decision Trees and Missing Data
Intrinsic Handling of Missing Data in Decision Trees
Tree Splits and Surrogate Splits
• Achieving Fairness without Imputation
Leveraging Decision Trees for Fair Predictions
Strategies to Mitigate Bias in Decision Trees
• Case Studies and Applications
Real-World Scenarios of Fair Decision Trees
Comparative Analysis with Imputed Models
• Practical Session: Implementing Fair Decision Trees
Hands-On Coding Exercise with Data Sets
Evaluating Fairness and Performance
• Advanced Topics in Fair Decision Tree Learning
Recent Research and Developments
Integrating Fair Trees into Larger Systems
• Conclusion
Summary of Key Concepts and Techniques
Future Directions in Fair AI and Missing Data Handling
• Further Reading and Resources
Suggested Academic Papers and Books
Online Platforms and Tools for Practice

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Subjects

Data Science