Historical Data Bias in AI: Identifying and Addressing Legacy Inequities
artificial-intelligence-ai.
Course Modules:
Module 1: Introduction to Historical Data Bias
What is historical bias in data?
Origins of bias: societal structures, data collection practices
Examples in education, credit scoring, healthcare, and policing
Module 2: Types of Bias in AI Systems
Representation bias
Measurement bias
Historical and societal bias
Label bias and feedback loops
Module 3: Auditing Datasets for Historical Bias
Analyzing demographic skew
Identifying proxy variables for race, gender, etc.
Visual and statistical techniques for bias detection
Module 4: Quantifying Disparity and Fairness
Metrics: statistical parity, disparate impact, equal opportunity
Group fairness vs. individual fairness
Fairness dashboards and bias reports
Module 5: Strategies for Bias Mitigation
Preprocessing: reweighting, sampling, data repair
In-processing: fairness-aware model training
Post-processing: outcome adjustments and explanation layers
Module 6: Capstone Project – Bias Analysis & Intervention Plan
Choose a dataset with historical context (e.g., loan approvals, school performance)
Audit the dataset for legacy bias
Propose and implement at least one mitigation technique
Submit a bias report with findings, visualizations, and reflections
Tools & Technologies Used:
Python, Pandas, Scikit-learn
AIF360 (IBM), Fairlearn
SHAP or LIME (for model explainability)
Jupyter Notebook / Google Colab
Target Audience:
AI and data science professionals
Policy makers and compliance officers
Ethical AI advocates and researchers
Students exploring fairness in machine learning
Global Learning Benefits:
Build awareness of how past injustices influence AI outcomes
Improve fairness and accountability in model design
Learn technical and ethical tools for bias mitigation
Promote trust, transparency, and inclusivity in AI systems
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