Algorithmic Auditing and Continuous Monitoring

Course details

As artificial intelligence becomes integrated into nearly every sector, establishing robust AI governance processes will be critical to maximizing its benefits and mitigating risks. Legislative acts like the EU AI Act in 2023—which will require any developer of a high-risk AI system seeking to enter the EU market to implement algorithmic auditing—and the NIST AI Risk Management Framework in the United States mean that companies that implement algorithmic auditing and continuous monitoring are poised to have a market advantage.

In this course, Brandie Nonnecke teaches you how to implement effective algorithmic auditing and continuous monitoring processes in your organization. Learn responsible AI governance, as Brandie covers mandatory and voluntary processes for auditing and monitoring AI systems, and provides case studies where you can work through an appropriate auditing and monitoring process.

Concepts

0. Introduction

01 - Algorithmic auditing and continuous monitoring uses
02 - Responsible AI principles and practices

1. Algorithmic Auditing and Continuous Monitoring

03 - What is algorithmic auditing
04 - What is continuous monitoring
05 - Voluntary and required algorithmic auditing
06 - Algorithmic auditing and the job market

2. Skills for Algorithmic Auditors and Continuous Monitoring Teams

07 - Establishing an effective governance structure
08 - Data collection and analysis
09 - Identifying and managing bias
10 - Setting up a continuous monitoring process

3. Case Study - Auditing and Continuous Monitoring Tools and Techniques in Practice

11 - Case study - RedTech 30
12 - RedTech 30 - Governance structure
13 - RedTech 30 - Data sampling
14 - RedTech 30 - Data and code review
15 - RedTech 30 - Testing and debugging
16 - RedTech 30 - Continuous monitoring

Conclusion

17 - Responsible AI resources