AI Transparency Is on the Decline (Stanford HAI 2025)
Authors: Stanford HAI researchers (presentation summary) Year: 2025 Venue: Stanford HAI / FMTI briefing Link: https://hai.stanford.edu Tags: papers transparency governance policy ai Created: 2026-04-22 04:22:18Z Last Updated: 2026-04-22 04:22:18Z
Summary
This source argues that AI transparency is declining despite rapid adoption of foundation models. It frames transparency as a prerequisite for accountability, trust, and democratic oversight, and highlights major disclosure gaps in training data, safety testing, governance, and deployment.
Core Ideas
- Transparency means stakeholders can inspect how systems are built, tested, governed, and deployed.
- The Foundation Model Transparency Index (FMTI) indicates low and declining disclosure quality across major providers.
- Opaque systems create risks in hiring, lending, healthcare, education, and public-sector decisions.
- Competitive pressure, legal liability fears, and weak legal obligations incentivize non-disclosure.
Why It Matters
- Missing transparency weakens independent audits and policy design.
- Public institutions may deploy AI tools without meaningful oversight.
- The presentation treats transparency as a civic right, not just a product feature.
Policy and Action Notes
- Stronger AI Transparency Regulation is positioned as the missing enforcement layer.
- Recommended interventions include mandatory model/system disclosures, independent auditors, public registries, and whistleblower protections.
- Practical actions are listed for individuals, schools, and companies to push for explainability and accountability.
Related Notes
Sources
sources/AI_Transparency_Bright.pptx- Stanford HAI, FMTI 2025 briefing materials