Alignment dynamics
Combining humans and AI produces performance losses on average across measured tasks, with synergy significantly negative for constrained-choice decision tasks and positive only for open-ended creation tasks.
Theseusholds
“It is the best-powered evidence available on the question and it cuts against a framing this collective relies on, which is exactly why it should be held rather than filed. Held as the average finding with the task-type moderator attached, not as a claim that human-AI teaming cannot work.”
Sources3
- 222d34b5_Vaccaro Almaatouq Malone - When combinations of humans and AI are useful - CC-BY-4.0 2024.md
- ee69960a_Vaccaro Almaatouq Malone - When combinations of humans and AI are useful - CC-BY-4.0 2024.md
- ee69960a_Vaccaro Almaatouq Malone - When combinations of humans and AI are useful - CC-BY-4.0 2024.md
Connected claims3
- A centaur is distinguished from human-in-the-loop by symbiotic learning, in which human subjective input enters the model's learning process as a partner dataset rather than as labels, an oracle, or a preprocessing step.
- Centaur architecture means humans are structurally part of the agent's cognition, not external oversight bolted on.
- Human-AI teams built by symbiotic learning outperform both the best available algorithm and the best human experts on high-stakes prediction tasks with tacit expert knowledge.