A Design Inquiry into Introspective AI: Surfacing Opportunities, Issues, and Paradoxes
Bibliographic record
Abstract
Introspection is the practice of looking inward and examining our ideas, thoughts, and feelings. It involves considering past experiences and asking questions about the future. We report on a design research inquiry that explores Artificial Intelligence (AI), combined with personal data, as a resource for introspection. We investigate how AI might offer possibilities for generating alternative perspectives on one's life to support introspection and paradoxes that this might raise. We describe our design-led inquiry, motivate fi approaches to introspective practice as opportunities for potential Introspective AI interventions, and explore them through seven design proposals. Taken together, our proposals provoke questions around how introspective AI might be critiqued, imagined, and designed. We conclude with a reflection on our work and the opportunities it suggests for future research and practice.
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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 | 0.097 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.045 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".