Certified algo bias auditor (CABA)

CERTIFIED ALGORTHIMIC BIAS AUDITOR PROGRAM

Here’s the Certified Algo Bias Auditor Program curriculum is designed to be deep, exhaustive, and highly practical, integrating coding examples across all phases—Detection, Assessment, and Mitigation.

Certified Algorithmic Bias Auditor Program

Total Learning Hours: 100 Hours (60 Hours Structured Learning + 40 Hours Practical Labs & Projects)

1. Introduction to Algorithmic Bias Auditing

What is Algorithmic Bias?
Historical & Real-World Cases of Algorithmic Bias
Ethical, Social, and Legal Implications
Regulatory Frameworks (EU AI Act, GDPR, CCPA, IEEE Standards, Fairness Directives)
Overview of the Three-Phase Auditing Approach

Phase 1: Bias Detection

2. Fundamentals of Bias in Machine Learning

Types of Bias (Selection, Measurement, Sampling, Label, Confirmation, etc.)
Bias in Data vs. Bias in Model
Group Fairness vs. Individual Fairness
Key Metrics for Bias Detection
Hands-on: Identifying Bias in Real-World Datasets

3. Exploratory Data Analysis (EDA) for Bias Detection

Statistical Disparity & Visualization Techniques
Disparate Impact Analysis
Data Skewness & Imbalance Detection
Hands-on: Bias Detection using Python (Pandas, NumPy, Matplotlib, Seaborn)

4. Bias Detection in Machine Learning Models

Bias in Feature Engineering & Data Preprocessing
Bias Propagation in Model Pipelines
Fairness Metrics:
- Demographic Parity
- Equalized Odds
- Predictive Parity
- Equal Opportunity
Hands-on: Implementing Fairness Metrics in Python (AIF360, Fairlearn)

Phase 2: Bias Assessment

5. Causal Analysis & Root Cause Identification

Causal Inference for Bias Auditing
Identifying Proxy Variables & Hidden Bias
Sensitivity Analysis & Counterfactual Testing
Hands-on: Implementing Causal Fairness Analysis

6. Auditing Bias in AI Models (Deep Dive)

Bias in Supervised vs. Unsupervised Learning
Auditing NLP Models (Gender & Racial Bias in LLMs)
Bias in Computer Vision (Facial Recognition, Object Detection)
Hands-on: Auditing GPT & Transformer Models for Bias

7. Benchmarking & Reporting Bias

Bias Reporting Frameworks (IBM AI Fairness 360, Google’s What-If Tool)
Standardized Audit Templates & Documentation
Hands-on: Generating Bias Reports & Visual Dashboards

Phase 3: Bias Mitigation

8. Preprocessing Techniques for Bias Mitigation

Reweighing & Rebalancing Datasets
Sampling Strategies (Oversampling, Undersampling)
Feature Encoding & Transformation for Fairness
Hands-on: Applying Preprocessing Techniques in Python

9. In-Processing Techniques for Fairness

Regularization for Fairness
Adversarial Debiasing
Fairness Constraints in Model Training
Hands-on: Implementing Adversarial Debiasing (AIF360, Fairlearn)

10. Post-processing Techniques for Bias Mitigation

Equalizing Outcomes via Post-processing
Recalibrating Model Predictions
Algorithmic Recourse Strategies
Hands-on: Implementing Post-processing Bias Mitigation

11. Bias Mitigation in Large-Scale AI Systems

Fairness in Federated Learning
Bias Challenges in Reinforcement Learning
Debiasing LLMs (Prompt Engineering, Model Distillation)
Hands-on: Bias Mitigation in Transformers & Deep Learning

Capstone Project & Certification

12. Real-World Bias Auditing & Compliance

Conducting a Full Bias Audit on a Live AI System
Compliance Readiness Assessment
Generating a Final Bias Audit Report

13. Ethical AI & The Future of Algorithmic Fairness

AI Ethics in Practice
The Role of AI Governance & Policy
Future Trends in Bias Auditing & Fairness AI

Practical Labs & Case Studies (40 Hours)

Case Study 1: Bias Detection in Loan Approval Models
Case Study 2: Gender Bias in Resume Screening AI
Case Study 3: Racial Bias in Facial Recognition Systems
Case Study 4: Bias Auditing of a Large Language Model
Case Study 5: Bias Mitigation in Credit Scoring AI

and more.

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