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We now know technology alone isn’t the solution and are led to think that culture is the biggest obstacle to analytics adoption. But that’s a sloppy excuse. Read why.

In the third installment of Parul Pandey’s AI risk management series, we explore various strategies and practices beyond traditional model risk management.

IIA's roundup of the best articles from around the web, curated and contextualized by unbiased analytics experts at IIA.

In the second article in our AI risk management series, we pivot our focus to a vital element in the context of ML systems: organizational processes.

This article introduces a three-part series on AI risk management. It discusses cultural competencies that can prevent and mitigate AI incidents, with a focus on promoting responsible AI practices.

As Vinita Bansal’s job shifted from coding to managing people, and then managing managers, she gleaned five crucial lessons through experimentation, trial, and error.

As your company maps out its 2024 AI strategy, use this powerful metaphor to consider the implications of centralized knowledge and striking a balance between knowledge graphs and LLMs.

Terminology in the data field is a mess. This article clears up the confusion – and responsibilities and skills required – using a series of Euler diagrams.

This article explores key factors for comparing machine learning solutions, providing an improved approach to model comparison beyond predictive power.