The unsolicited algorithm: unveiling gendered harms and (non)consent in apple iOS features
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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