Black box
The problem of a system known only from the outside — its inputs and outputs visible, the conversion between them closed to inspection — and the argument over whether the demand to open it should fall on machines, on minds, or on both. The method is old: W. Ross Ashby's 1956 cybernetics gave the black box its general statement and applied it in the same paragraphs to a sealed circuit and to a damaged brain. The modern machine form runs through the interpretability literature — the case for replacing opaque models (Rudin), the charge that "interpretability" is itself underspecified and that humans are no more transparent (Lipton), the post-hoc explanation methods (Ribeiro's LIME; Wachter's counterfactuals), and the mechanistic programme that claims to reverse-engineer a network feature by feature (Olah's circuits; Anthropic's Golden Gate Claude). The human form is the confabulation literature — Nisbett and Wilson on the absence of introspective access, Gazzaniga's left-hemisphere interpreter, Haidt's post-hoc moral reasoning. The two halves meet where a model's stated reasoning turns out not to be its real reason (Turpin), and where standard analysis methods fail on a chip whose wiring is fully known (Jonas and Kording). The stakes are drawn by Pasquale on the politics of algorithmic secrecy and by Dennett, whose intentional stance asks why a machine should owe a legibility that no mind has ever provided.
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◊ concepts
Algorithmic opacity · Black-box method · Confabulation · Counterfactual explanation · Explainability · Explanation faithfulness · Intentional stance · Interpretability · Mechanistic interpretability · Post-hoc rationalisation
❡ people
Adly Templeton · Been Kim · Brent Mittelstadt · Carlos Guestrin · Chris Olah · Chris Russell · Cynthia Rudin · Daniel C. Dennett · Eric Jonas · Ethan Perez · Finale Doshi-Velez · Frank Pasquale · Jonathan Haidt · Julian Michael · Konrad Paul Kording · Marco Tulio Ribeiro · Michael S. Gazzaniga · Miles Turpin · Richard E. Nisbett · Sameer Singh · Samuel R. Bowman · Sandra Wachter · Shan Carter · Tim Miller · Timothy D. Wilson · Trenton Bricken · W. Ross Ashby · Zachary C. Lipton
§ sources
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- Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
- Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet
- Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting
- Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR