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TL;DR • This featured resource is from the U.S. National Association of Insurance Commissioners • It...
TL;DR • Checklists help cover the basics and help maintain consistency. • But not everything can be ...
TL;DR • Technical stakeholders need detailed explanations. • Non-technical stakeholders need plain l...
TL;DR • Algorithmic systems create challenges in balancing explainability with privacy and confident...
TL;DR • This is a new monthly feature, with regular articles in other weeks. • This month’s research...
TL;DR • Explainability is necessary to build trust in AI systems. • There is no universally accepted...
TL;DR • Algorithmic processes are often complicated by intricate data flows and transformations. • D...
TL;DR • Complexity must be actively managed rather than passively accepted. • Data relevance directl...
TL;DR • Why Explainability Matters: It builds trust, is needed to meet compliance obligations, and c...
TL;DR • Testing is a core basic step for algorithmic integrity. • Testing involves various stages, f...
TL;DR • Third-party assurance for algorithm integrity varies based on the nature of the relationship...
This is the third guest interview episode of Algorithm Integrity Matters.
TL;DR • AI literacy is growing in importance (e.g., EU AI Act, IAIS). • AI literacy needs vary acros...
This is the second guest interview episode of Algorithm Integrity Matters.
This is the first guest interview episode of Algorithm Integrity Matters.
TL;DR • Public AI audit reports aren't universally required; they mainly apply to high-risk applicat...
TL;DR • Knowing the basics of substantive testing vs. controls testing can help you determine if the...
TL;DR • Ongoing education helps everyone understand their role in responsibly developing and using a...
TL;DR • Outcome-focused accuracy reviews directly verify results, offering more robust assurance tha...
TL;DR • Start with five key items: Algorithm Inventory; Risk Assessment; Policies (with Procedures, ...
