Human–Machine Social Systems: Test and Validation via Military Use Cases
Bibliographic record
Abstract
Global commercial leaders (e.g. Google, Amazon, and Toyota) and governments around the world are heavily investing in intelligent, bidirectional interactions between humans and technologies that involve complex social interactions. The military sector, in particular, is investing in modernization strategies that target artificial intelligence/machine learning (AI/ML) techniques that lend themselves to human–machine teaming in order to prepare for a future of multidomain operations. As with the pioneering empirical approach to human assessment and selection by global military leaders post–World War I [16], the immediacy, complexities, size, diversity, and resource capability of military use cases can generate the foundational underpinnings for shared problems, such as human-machine systems (HMS). When executed with strategic partners (e.g. commercial sector, partner nations), these underpinnings can be extrapolated and validated in multiple application domains. This chapter outlines key social cognitive complexities best examined in situ with real users, and highlights collaboration opportunities with the U.S. military (e.g. Army Project Convergence) as one potential path for in situ test and validation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".