Reimagining Human-AI Relationships: A Positive Future for Chatbots, Social AI, and the Phygital Self
Bibliographic record
Abstract
There are many valid concerns about Artificial Intelligence (AI) that must be taken seriously. However, historically, technological progress has sometimes led to unexpected benefits. This essay begins with an imaginative fictional future-history, followed by an academic discussion. The fictional narrative acts as a jumping-off point for exploring the potential benefits of social AI, or AI that interact socially with humans, (e.g., ChatGPT, Claude, Grok etc) in three areas: (1) social AI agents as relationship partners, (2) how our interactions with AI might affect our human relationships, and (3) social AI's influence on shaping self-identity. Consistent with macromarketing, we examine how social AI in marketing might affect people's lives far beyond the economic sphere. And in keeping with the theme of this special issue on phygital marketing, we conclude with suggestions on how these dynamics could impact phygital (physical and digital) marketing strategies and consumption trends.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".