AILP — AI 学习许可协议

01 · 定义

AILP operationalizes the AIRS spectrum for the specific question of learning. It distinguishes acts that binary crawler rules collapse together: being read is not being learned from; being retrieved is not being trained on; fine-tuning is not distillation; verbatim memorization is different from statistical internalization. AILP lets a publisher declare, in a single machine-readable file, per-dimension permissions — access, indexing, inference input, embedding, training, fine-tuning, distillation, memory, output, attribution and compensation — each as allowed, denied, or license-required. An AILP grant answers "may AI learn from this content?" only — it does not answer "may this Agent invoke a tool, write to a database, or publish?" (AARS), nor "who authorized this Agent?" (AADP).

02 · 目的

• 用维度级精度回答“AI 能否学习这些内容”,而不是一个爬取位。
• 把学习许可(内化进模型)与访问许可(抓取页面)分开。
• 支持多种补偿模式:免费、非商用、需授权、收益分成。
• 让开放成为机器可执行的声明——善意才不会被当作法律不确定性而被丢弃。

03 · 范围

04 · 机器可读示例

学习许可声明 — /ai/rights-spectrum.json(AILP profile)

{
  "version": "0.1",
  "protocol": "AILP",
  "publisher": "example.org",
  "default": {
    "access": "allowed",
    "indexing": "allowed",
    "inference_input": "allowed",
    "embedding": "allowed",
    "training": "license_required",
    "fine_tuning": "license_required",
    "distillation": "denied",
    "verbatim_memory": "denied",
    "attribution": "required",
    "compensation": "contact"
  },
  "contact": "licensing@example.org"
}

05 · 限制与边界

• 学习许可的语义在技术上最难验证——AILP 声明意图,尚无法证明合规。
• 维度清单仍是草案;训练、微调、蒸馏之间的边界仍有争论。
• 学习许可的法律效力因司法辖区而异,尚无定论。
• v0.1.1: AILP(Resource,ContentUse)=Allow does not imply AARS(Actor,Action)=Allow, and does not imply AADP(Principal,Actor,Authority)=Valid — a system exposing both content and tools must evaluate them separately.