A Multi-Tiered AI Accessibility Model (MAIAM): Proportionate Access and Capability Limits for Comprehensive Frontier AI Safety

Key points

• Most debate about releasing capable AI systems treats the choice as binary: keep the model behind an API, or publish the weights.1
• Tier 1, AI for Learning, is broadly accessible and deliberately capability-bounded.1
• We show why publishing frontier-class weights forfeits every safety lever a developer has after release, and, using evidence that safety fine-tuning can be stripped from open weights for a few hundred dollars, why an evaluation of the base model says little about its derivatives.1
• We give criteria for assigning a system to a tier along capability, autonomy, domain-consequence, and usage/safety axes, and combine MAIAM with the SAFE AI Operating Point into a two-part framework: a Capability Boundary that fixes the safety limits not to be crossed, and an Accessibility Boundary that fixes who may obtain a capability below that limit.1

Sources (1)

• [1]A Multi-Tiered AI Accessibility Model (MAIAM): Proportionate Access and Capability Limits for Comprehensive Frontier AI Safety
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 08:52 PM

  We propose a Multi-Tiered AI Accessibility Model (MAIAM) that sorts access by purpose, safety, and capability instead of by release artifact.

  Most debate about releasing capable AI systems treats the choice as binary: keep the model behind an API, or publish the weights.

Extractive summary: sentences quoted from the sources.