Tag: embeddings

Population genetics has Mitochondrial Eve: the ancestor every living human's mitochondrial DNA traces back to. Fine print has an equivalent question. Legal text is almost never written fresh — it descends. This is a technical orientation on measuring that relatedness: content hashes for identity, winnowing fingerprints for inherited wording, on-prem SLM embeddings for kinship of meaning, and a dated derivation graph for descent — across 6,738 documents and 125,295 clauses. The search for the common ancestor turns up something real: the warranty disclaimer, carried by nearly every license measured.

Terms of service are revised for two decades, and clauses come and go. Scoring every archived revision of YouTube, Reddit, and Twitter against the same 34 consumer issues — and measuring how much each revision changed — surfaces what a single snapshot hides: rewrites that leave the length untouched, and clauses removed for years that quietly return. YouTube's terms drop machine-learning language in the 2010s and bring it back, spelled out, in July 2022 — a revision that replaced 92% of the document's wording with a net length change of 21 words.

A word count tells you a terms-of-service document got longer. It does not tell you what the document covers. This is a different measurement: a fixed list of 34 consumer issues, checked against real terms-of-service and privacy documents, with every clause scored three ways — how closely it matches the issue, how much text it spends on it, and how harshly it reads. It starts with three documents read closely and ends with an interactive leaderboard of sixty-four, ranked by what they actually address.