'Credit assignment' names both the sociology of scientific credit and backpropagation's technical problem — and Schmidhuber sits on the hinge

credit-assignmentsociology-of-scienceschmidhuberbackpropagationhistory-of-mlpriority-disputecross-domain-bridge

The phrase "credit assignment" carries two nearly unrelated meanings that converge on the history of AI. In the sociology of science, credit assignment is the (mis)allocation of recognition for a discovery — the subject of the Merton–Stigler tradition. In machine learning, "the credit-assignment problem" is the technical question of how to apportion responsibility for an outcome across many decisions or weights, named by Minsky in 1961 (claim-minsky-1961-named-credit-assignment); backpropagation is the method most associated with solving its structural form (myth-structural-credit-assignment-from-minsky-1961).

Jürgen Schmidhuber occupies the hinge between the two senses. He opens his Annotated History of Modern AI and Deep Learning (arXiv:2212.11279) with the technical definition — "Machine learning (ML) is the science of credit assignment" — while the same document runs the field's most sustained campaign in the sociological sense: reassigning priority for deep-learning ideas from famous names to earlier, more obscure ones. The genealogy he builds (Linnainmaa → Dreyfus → Werbos → …) is itself an exercise in reversing misassigned credit — the move examined at claim-dreyfus-1973-lineage-link-uncorroborated and claim-werbos-1974-no-credit-assignment-language.

That genealogical move — reducing an apparent breakthrough to descent from prior work — is the same one Tertius Chandler states as a general law (claim-chandler-simultaneous-discoveries-are-incremental-repackagings), applied here to backpropagation. It stands against the Ogburn–Thomas/Merton reading of multiple discovery, in which convergent inventions are genuinely independent and "in the air." So the two senses of credit assignment are not merely a pun: Schmidhuber's technical subject (apportioning credit inside a network) and his historiographic method (apportioning credit across a field) are the same operation at two scales. See moc-backpropagation-origins.