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Responsible AI in the Enterprise

You're reading from  Responsible AI in the Enterprise

Product type Book
Published in Jul 2023
Publisher Packt
ISBN-13 9781803230528
Pages 318 pages
Edition 1st Edition
Languages
Authors (2):
Adnan Masood Adnan Masood
Profile icon Adnan Masood
Heather Dawe Heather Dawe
Profile icon Heather Dawe
View More author details

Table of Contents (16) Chapters

Preface 1. Part 1: Bigot in the Machine – A Primer
2. Chapter 1: Explainable and Ethical AI Primer 3. Chapter 2: Algorithms Gone Wild 4. Part 2: Enterprise Risk Observability Model Governance
5. Chapter 3: Opening the Algorithmic Black Box 6. Chapter 4: Robust ML – Monitoring and Management 7. Chapter 5: Model Governance, Audit, and Compliance 8. Chapter 6: Enterprise Starter Kit for Fairness, Accountability, and Transparency 9. Part 3: Explainable AI in Action
10. Chapter 7: Interpretability Toolkits and Fairness Measures – AWS, GCP, Azure, and AIF 360 11. Chapter 8: Fairness in AI Systems with Microsoft Fairlearn 12. Chapter 9: Fairness Assessment and Bias Mitigation with Fairlearn and the Responsible AI Toolbox 13. Chapter 10: Foundational Models and Azure OpenAI 14. Index 15. Other Books You May Enjoy

Monitoring and mitigating drift in ML models

All three types of drift (model, data, and concept) are important to measure, as they impact the performance and accuracy of ML models. Monitoring and mitigating each type of drift is essential to maintain model performance over time:

  • As discussed, model drift occurs when a model’s performance degrades as it becomes outdated due to changes in the underlying data distribution or concept.

Mitigation: Regularly retrain the model with fresh, representative data to maintain its performance. For example, retrain a sales prediction model with new sales data to capture recent trends and changes in customer behavior.

  • Data drift occurs when the input data distribution changes over time, making the model’s training data less representative of the current data.

Mitigation: Continuously monitor the distribution of input features and compare them to the training data. If significant deviations are detected,...

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