The unsolicited algorithm: unveiling gendered harms and (non)consent in apple iOS features
Bibliographic record
Abstract
Algorithms are pivotal in shaping our online and offline experiences, yet they can inadvertently perpetuate hostile digital environments for users. This paper introduces the concept of the unsolicited algorithm, referring to algorithms that deliver content, recommendations, or actions without explicit user consent and awareness. Drawing from a technofeminist paradigm, we explore the repercussions of such algorithms on iPhone users experiencing marginalization, especially concerning gender. To do so, we present case studies involving the iPhone iOS features For You, which can resurface distressing memories for gender-based violence survivors, and Airdrop, commonly misused for the non-consensual sharing of explicit content. By proposing the concept of the unsolicited algorithm, we encourage critical discourse on the ethical implications of automated configuration in decision-making systems and emphasize the need to prioritize user consent and transparency in algorithm and affordance design. We also advocate for algorithm designers to revisit policies, implement algorithmic interventions and consider the vulnerabilities of marginalized users while prioritizing their agency and well-being.
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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 | 0.022 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.043 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".