Moral Cultivation and the Quantified Self: Assessing the Self Understanding of Data Profiles Generated by AI with a Virtue Ethics Approach
Bibliographic record
Abstract
Supporters of personal data collection and analysis contend that data profiles generated from AI algorithms represent a desirable pursuit for the quantified self. Proponents of the quantified self claim that AI-generated data profiles represent a more objective and truthful account of individual lives. They also argue that the quantified self fosters human flourishing by supplying individuals with data-informed accounts about their lives. First, I will trace the technological origins of the quantified self. Second, the first claim will be critiqued by demonstrating that the quantified self presents a reduced and subjectively abstracted picture of human life. Third, the second claim will be questioned, from a virtue ethics approach, to show how the quantified self’s reduced concept of self-examination is detached from self-cultivation. Fourth, a neo-Aristotelian virtue ethics framework will be applied to argue that the self-knowledge sought by the quantified self hinders agents’ practical reasoning.
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How this classification was reachedexpand
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".