The Critical Human Elements in Using Artificial Intelligence in Comparative Cognition Studies
Bibliographic record
Abstract
The past 5 years have seen a revolution in the use of artificial intelligence (AI) across a wide range of disciplines, from the sciences to education to the performing arts (Haenlein & Kaplan, 2019).Its ability to change how society collects, distills, and interprets information offers immense promise for potential breakthroughs while requiring reflection on the ethics and limitations of its capacity.For nonspecialists, AI is the ability of computer programs to emulate human decision making and perform tasks in everyday environments.Within the scope of AI falls machine learning (ML), which refers to the specific technologies or algorithms that enable computer systems to identify patterns, make decisions, and improve accuracy through increased experience and interaction with the data of interest (Kok et al., 2009).In this article, we provide examples of how AI is transforming the field of animal cognition and behavior, especially within the discipline of animal communication.We discuss several of AI's contributions to deciphering how animals exchange and interpret information and consider what we may risk losing along the way.We hope to begin an evolving conversation about the use of AI in studies of animal behavior and reflect on whether AI can truly enhance meaningful outcomes in our pursuit of understanding nature.
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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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.043 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".