The Relational Tradeoff Model: The Effects of Socially Interactive Artificial Intelligence (AI) in Human-AI Relationships
Bibliographic record
Abstract
Despite the proliferation of artificially intelligent systems capable of social interaction, how and why social interaction influences users over time remains poorly understood. We draw on theories of technology adoption and research in affective computing, social psychology, and management to introduce the concept of human-AI relationships involving interdependence, temporality, and intensity. We develop the Relational Tradeoff Model, extending current theorizing on technology adoption by accounting for a critical third factor in addition to cognitive acceptance and behavioral use: human subjective well-being. The model reveals an important unexplored tradeoff in relationships with socially interactive AI: short-term acceptance and use gains but long-term subjective well-being costs for trust, psychological safety, and emotional labor, depending on AI social function and exacerbating and mitigating individual and relational factors. We discuss implications and suggestions for future exploration, including intrapersonal, interpersonal, and team relational dynamics and evolving expectations of AI in organizations.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".