Navigating the Advent of Human-Machine Teaming
Bibliographic record
Abstract
The objective of this panel was to discuss issues related to human-machine (or human-agent) teaming (HMT). Panelists were selected to represent diverse interests and backgrounds (i.e., defense, industry, and academia). Chris Brill provided opening remarks to frame the discussion and introduce the panelists. He then raised several questions related to HMT, such as what is HMT, what level of autonomy is required for HMT, and how do we develop trust in autonomous teammates that learn, change, and potentially, individuate. Missy Cummings built on the issue of learning systems, addressing challenges of certifying systems that, as a function of learning, may cease to be known quantities. Bill Evans spoke to the need for transparency in human-agent teaming. Joseph Lyons addressed social factors in HMT. Peter Hancock detailed his concerns about whether forays into HMT are even advisable, particularly as doing so may lead to dehumanization, or worse, volitional demotion of humans from our current status as apex lifeforms on Earth. Lastly, Kevin Oden expanded the discussion of trust in autonomous systems, while also providing thoughts on how to best leverage human capabilities in the context of HMT. The panel then turned to facilitated discussion with panelists and audience members, constituting the majority of the session time. The session concluded with panelists summarizing their thoughts on how HF/E professionals can or should play a role in the advent of HMT.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".