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Algorithmic Auditing and Continuous Monitoring

Via LinkedIn Learning

LinkedIn Learning based on 86 ratings

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Overview

This course explores algorithmic auditing techniques and continuous monitoring to ensure AI system fairness, transparency, and compliance. Learners study methodologies for detecting bias, verifying model performance, and maintaining ethical standards throughout AI deployments. The curriculum includes practical frameworks for automating audits and integrating monitoring tools into AI workflows. Designed for AI practitioners, auditors, and compliance officers, this course equips participants to uphold accountability and trust in AI applications.

Syllabus

Introduction
  • Algorithmic auditing and continuous monitoring uses
  • Responsible AI principles and practices
1. Algorithmic Auditing and Continuous Monitoring
  • What is algorithmic auditing?
  • What is continuous monitoring?
  • Voluntary and required algorithmic auditing
  • Algorithmic auditing and the job market
2. Skills for Algorithmic Auditors and Continuous Monitoring Teams
  • Establishing an effective governance structure
  • Data collection and analysis
  • Identifying and managing bias
  • Setting up a continuous monitoring process
3. Case Study: Auditing and Continuous Monitoring Tools and Techniques in Practice
  • Case study: RedTech 30
  • RedTech 30: Governance structure
  • RedTech 30: Data sampling
  • RedTech 30: Data and code review
  • RedTech 30: Testing and debugging
  • RedTech 30: Continuous monitoring
Conclusion
  • Responsible AI resources
Algorithmic Auditing and Continuous Monitoring
Go to Class

via LinkedIn Learning

1 hour 3 minutes

Certificate Available

English

On-Demand

Instructor

Brandie Nonnecke

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