Between automated memory and history: blocking ‘sensitive locations’ from Apple Memories
Bibliographic record
Abstract
Abstract In April 2022, journalists at the tech website 9to5Mac discovered that photographs taken at sites related to the Holocaust would no longer appear in the Memories feature of Apple's Photos app. This article examines how news of this decision was received by the public through analysis of the comment section that followed the original 9to5Mac post. The perspectives on display in this public forum provide insight into the evolving public perception of automated memory technologies and the potential consequences of their use. Through this analysis, several interrelated areas of public concern emerge. These include the boundaries of platform intervention for governing access to content, the subjective qualities of personal photographs, and the metrics upon which algorithmic memory systems operate. Though opinions vary, this comment section captures an illustrative range of sentiment towards Apple Memories and this intervention into the memories of its users. This range demonstrates a degree of scepticism, alarm, and dissatisfaction rising among users who are increasingly aware of how algorithms are influencing their memories.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".