Aiconomy

AI Transparency

The principle that AI systems should be open about how they work, what data they were trained on, and how they make decisions, enabling meaningful oversight and accountability.

The Stanford Foundation Model Transparency Index found that no major AI company scored above 75% on transparency indicators in 2024. The EU AI Act mandates transparency requirements including disclosure of AI-generated content and documentation of training data. The tension between transparency and trade secrets is a central challenge — companies argue that full disclosure of training data and model architecture would undermine competitive advantage. Transparency is closely linked to interpretability (understanding how models make decisions) and accountability (holding developers responsible for outcomes).

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AI Economy Pulse

Every Friday: the 3 AI data points that actually matter this week. Free, forever.

Built on data from Stanford HAI, IEA, OECD & IMF

Latest: “AI Investment Hits $42B in Q1 2026 — Here's Where It Went”

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