CONCEPT
Data Dignity
The principle, developed by Lanier and E. Glen Weyl, that every person whose data contributes to a digital system deserves to be acknowledged and compensated for that contribution — the constructive counterpart to the diagnosis of siren servers.
Data dignity is Lanier's prescription, arrived at after fifteen years of diagnosing the extractive architecture of the digital economy. The principle is elementary: labor that creates value deserves compensation. Its application to AI is radical: if large language models derive their capability from the accumulated labor of millions of human contributors, those contributors are owed both acknowledgment and a share of the value their data generates. Data dignity rejects the framing of training data as a free resource to be harvested. It insists that data is the product of specific human effort and that the people performing that effort retain moral and economic claims on what is built from it. The principle has technical, economic, and institutional dimensions — each of them feasible, each of them politically difficult, each of them currently unbuilt because the beneficiaries of the status quo have no reason to build them.
In The You On AI Field Guide
Lanier introduced data