Humans vs. Machines: Exploring the Social and Ethical Frontiers of Technology
Bibliographic record
Abstract
As human-machine interactions become increasingly prevalent, understanding the dynamics of these exchanges is critical for both individual and societal outcomes. This symposium brings together six cutting-edge papers exploring how humans interact with machines and each other, the individual-level and societal consequences of these interactions, and the ethical implications of machine involvement in human life. By examining a range of contexts—from conversational dynamics to civic engagement and ethical dilemmas—this symposium sheds light on the opportunities and challenges posed by the integration of technology into social, emotional, and decision-making domains. The first half of the symposium (Papers 1–3) focuses on interpersonal dynamics between humans and machines, examining short-term interactions, emotional connections, and the broader implications of AI engagement for well-being and loneliness. The second half of the symposium (Papers 4–6) explores systemic societal impacts and ethical concerns, including how AI amplifies biases, facilitates voter engagement, and challenges informed consent. Together, these papers demonstrate the multifaceted effects of human-machine interactions, offering theoretical and practical insights.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.047 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".