Envisioning and Understanding Orientations to Introspective AI: Exploring a Design Space with Meta.Aware
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introspection is the practice of looking inward for ongoing self-examination. It involves considering one's past experiences and asking questions about the present and future. Our work investigates how AI could open new possibilities for supporting introspective experiences. Adopting a design fiction approach, we created a fictional company called Meta.Aware to contextualize 4 different Introspective AI product concepts in the form of video sketches. We used the Meta.Aware platform to conduct interviews with 17 participants, using the 4 concept videos as prompts for discussion. Participants had a range of reactions related to perceived benefits and tensions in this emerging design space. We interpret these results to outline future design directions for mobilizing AI as a resource to support introspective experiences over time, as well as to reflect on issues and dilemmas bound to this emerging design space.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it