Historical Data Bias in AI: Recognizing and Correcting Legacy Inequities
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Course Modules:
Module 1: Understanding Historical Bias in Data
What is historical bias and how does it emerge?
Real-world case studies (e.g., housing, healthcare, policing)
How legacy systems shape future predictions
Module 2: Common Types of Data Bias
Representation bias
Label and measurement bias
Historical injustice encoded in training data
Module 3: Dataset Auditing for Historical Bias
Demographic distribution analysis
Proxy detection and redlining
Tools for visualizing and flagging skewed patterns
Module 4: Fairness Metrics and Detection Tools
Equal opportunity, demographic parity, disparate impact
Using AIF360 and Fairlearn for fairness evaluation
Designing dashboards for monitoring historical patterns
Module 5: Techniques to Mitigate Historical Bias
Preprocessing: reweighting, balancing, and data transformation
In-processing: fairness-constrained learning
Post-processing: output adjustment and bias correction
Module 6: Capstone Project – Audit and Redesign
Select a biased dataset (e.g., admissions, loan approvals, resumes)
Audit it using learned techniques
Propose and document an ethical AI redesign plan
Tools & Technologies Used:
Python, Pandas, NumPy
Fairlearn, AIF360, SHAP
Jupyter Notebook / Google Colab
Data visualization: Seaborn, Matplotlib
Target Audience:
AI developers and data scientists
Ethics officers and compliance teams
Policy makers and researchers
Students exploring fairness and responsible AI
Global Learning Benefits:
Prevent AI from repeating past injustices
Build fairer systems for hiring, healthcare, education, and more
Gain skills for ethical AI development and governance
Align AI innovation with global equity and inclusion goals
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