Introduction to Data Governance for Machine Learning Systems: Fundamental Principles, Critical Practices, and Future Trends

© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2024

A. Nandan PrasadIntroduction to Data Governance for Machine Learning Systemshttps://doi.org/10.1007/979-8-8688-1023-7_6

6. Model Transparency and Interpretability

Aditya Nandan Prasad¹  

(1)

Bangalore, Karnataka, India

 

Model transparency and interpretability are central themes in the governance of machine learning (ML) systems. These concepts refer to the ability to understand and explain how machine learning models make decisions and are crucial for ensuring that ML systems operate in an ethical and accountable manner. Transparency involves providing insight into the inner workings and processes of ML models, while interpretability refers to the degree to ...