Navigating the intricacy and the anatomy of ML governance

ML doesn’t operate solely by using algorithms and the data they ingest. Instead, its essence lies in constructing models responsibly, a task underpinned by governance. Just as governance has been the bedrock of the realm of data, it’s equally crucial for ML, especially in aspects such as accountability, standardization, compliance, quality, and clarity. Let’s discuss this topic in greater detail in the following sections.

ML governance pillars

Unlocking ML’s potential is rooted in ensuring that models meet the following criteria:

• Aligns with relevant regulatory and ethical benchmarks
• Exhibits consistent outcomes and performance
• Illuminates their development and implications in a transparent way
• Can undergo regular quality assessments and updates
• Adheres to standard documentation and cataloging protocols

While adherence to industry-specific regulations sets the...