Master Study AI

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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