Editing Ai Governance (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
The governance challenge for AI is unique because AI systems:
# are opaque (black-box decisions are hard to audit),
# scale rapidly (a single model can affect millions in seconds),
# evolve continuously (models are updated, fine-tuned, deployed in new contexts),
# are dual-use (the same technology enables both beneficial and harmful applications), and
# cross jurisdictions (a model trained in the US might be deployed in Europe, regulated by EU law).

'''The EU AI Act risk pyramid''': The EU AI Act categorizes AI by risk level and applies proportionate requirements:
- '''Unacceptable risk''' (BANNED): Social credit scoring, real-time public biometric identification, emotion recognition in workplaces, manipulative AI targeting vulnerable groups.
- '''High risk''' (STRICT REQUIREMENTS): Medical AI, hiring algorithms, credit scoring, border control, critical infrastructure. Requires risk management, data governance, transparency, human oversight, accuracy, robustness.
- '''Limited risk''' (TRANSPARENCY OBLIGATIONS): Chatbots, deepfakes — must disclose AI nature.
- '''Minimal risk''' (NO REQUIREMENTS): Spam filters, video games AI.

'''General Purpose AI (GPAI) provisions''': Foundation models like GPT-4 and Gemini are regulated as GPAI. Providers must publish technical documentation, comply with copyright law, publish training data summaries. "Systemic risk" models (above 10^25 FLOPs training compute) face additional red-teaming and incident reporting requirements.

'''US approach''': More fragmented — Executive Orders, sector-specific guidance (FDA for medical AI, FTC for commercial AI, CFPB for credit AI), state laws (Illinois AEIA for employment AI, Colorado for insurance AI). NIST AI RMF provides voluntary framework. No comprehensive federal AI law as of 2024.
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