Safer Algorithmically-Mediated Offline Introductions: Harms and Protective Behaviors

People are increasingly introduced to each other offline thanks to online platforms that make algorithmicallymediated introductions between their users. Such platforms include dating apps (e.g., Tinder) and in-person gig work websites (e.g., TaskRabbit, Care.com). Protecting the users of these online-offline systems requires answering calls from prior work to consider 'post-digital' orientations of safety: shifting from traditional technological security thinking to consider algorithm-driven consequences that emerge throughout online and offline contexts rather than solely acknowledging online threats. To support post-digital safety in platforms that make algorithmically-mediated offline introductions (AMOIs), we apply a mixed-methods approach to identify the core harms that AMOI users experience, the protective safety behaviors they employ, and the prevalence of those behaviors. First, we systematically review existing work (𝑛 = 93), synthesizing the harms that threaten AMOIs and the protective behaviors people employ to combat these harms. Second, we validate prior work and fill gaps left by primarily qualitative inquiry through a survey of respondents' definitions of safety in AMOI and the prevalence and implementation of their protective behaviors. We focus on two exemplar populations who engage in AMOIs: online daters (𝑛 = 476) and in-person gig workers (𝑛 = 451). We draw on our systematization and prevalence data to identify several directions for designers and researchers to reimagine defensive tools to support safety in AMOIs.

CCS Concepts: • Human-centered computing → Empirical studies in HCI.