Applied AI Auditing in Python

Learners how to conduct audits to quantify unfairness and disparities to uncover bias and develop fairer AI systems.

Introduction

• Get responsible with AI: Auditing AI systems in Python
• What you should know
• Using the exercise files and datasets

1. Introduction to Applied AI Auditing

• AI auditing for compliance and fairness
• AI audit stakeholders
• Localized fairness and compliance

2. Data Auditing

• How to collect benchmark datasets
• Ethical and inclusive data collection
• Explore a dataset for representation
• Data auditing example
• Challenge: Audit a dataset
• Solution: Methods for increasing representation in data

3. AI Model Auditing

• Tools for AI audits
• Scoping an AI audit
• Model audit setup
• Audit your classifier for fairness
• Challenge: Audit a classifier
• Solution: Audit a classifier

4. System Audits and Error Analysis

• Red teaming
• Error analysis
• Challenge: Error analysis
• Solution: Error analysis

5. Audit Artifacts

• Making audit recommendations
• Sharing audit results and increasing accountability
• Algorithmic recourse
• Algorithmic design history file

Conclusion

• Thanks for watching