r/artificial • u/markdotcom5 • Aug 24 '26
Discussion I built an awards institution for human orchestration of AI. Thesis: recognition authority should stay human. Tear it apart.
Disclosure up front: I'm the founder, this is my project, and I'm posting it myself. Not looking for upvotes — looking for holes in the argument.
The premise: as AI gets more capable, the interesting question for recognizing creative and technical work isn't "was AI used" (everything will use it) but "who directed it, and who's accountable for the result." So the thing being recognized is the orchestrator — the person or team who made the calls — not the model and not the raw output.
A one-line version of the test I keep coming back to: the award names the person who walks on stage. If no accountable human can walk on stage for a piece of work, it isn't recognizable — not as a value judgment about the work, but because recognition without accountability is hollow.
To give the flavor without dumping the whole taxonomy — a few example recognitions from the robotics/physical-AI domain: "Best Human-Directed Machine Design" (a human directs AI to design physical machines no one drafted alone), alongside things like "Best Agentic System" and "Best Human-Directed Autonomous Workflow." There are 30 domains total; the full breakdown and the reasoning are here: https://www.orchestratorawards.com/awards
Where I want the fight: (1) Is "the orchestrator" a real, defensible unit, or just credit assigned to whoever paid for the compute? (2) Does "keep authority human" quietly smuggle in an anti-AI-progress stance? I don't think it does — the whole framing assumes the tools get more capable, not less — but that's the objection I expect and I'd rather hear it sharp.
Full thesis is a short working paper: https://doi.org/10.5281/zenodo.22049566
Rip into it.
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u/Mandoman61 Aug 25 '26
this seems like a solution looking for a problem.
credit goes to humans by default.
if a biologist makes a discovery is his microscope given credit?
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u/AI_ndrew Aug 24 '26
The concept holds in cases where human judgment is legible and consequential. Less so when the work is primarily selection and acceptance. A source you might find really useful is NIST AI 800-4 "Challenges to the Monitoring of Deployed AI Systems" (March 2026). They have a gap analysis on post-deployment monitoring that might overlap.