The unsolicited algorithm: unveiling gendered harms and (non)consent in apple iOS features

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.