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This paper considers how subjectivities are enlivened in algorithmic systems. We first review related literature to clarify how we see “subjectivities” as emerging through a tangled web of processes and actors. We then offer two case studies exemplifying the emergence of algorithmic subjectivities: one involving computational topic modeling of blogs written by parents with children on the autism spectrum, and one involving algorithmic moderation of social media content. Drawing on these case studies, we then articulate a series of qualities that characterizes algorithmic subjectivities. We also compare and contrast these qualities with a number of related concepts from prior literature to articulate how algorithmic subjectivities constitutes a novel theoretical contribution, as well as how it offers a focal lens for future empirical investigation and for design. In short, this paper points out how certain worlds are being made and/or being made possible via algorithmic systems, and it asks HCI to consider what other worlds might be possible. 

Neophobia, or aversion to novelty, is important for adaptability and survival as it influences the ways in which animals navigate risk and interact with their environments. Across individuals, species and other taxonomic levels, neophobia is known to vary considerably, but our understanding of the wider ecological drivers of neophobia is hampered by a lack of comparative multispecies studies using standardized methods. Here, we utilized the ManyBirds Project, a Big Team Science large-scale collaborative open science framework, to pool efforts and resources of 129 collaborators at 77 institutions from 24 countries worldwide across six continents. We examined both difference scores (between novel object test and control conditions) and raw data of latency to touch familiar food in the presence (test) and absence (control) of a novel object among 1,439 subjects from 136 bird species across 25 taxonomic orders incorporating lab, field, and zoo sites. We first demonstrated that consistent differences in neophobia existed among individuals, among species, and among other taxonomic levels in our dataset, rejecting the null hypothesis that neophobia is highly plastic at all taxonomic levels with no evidence for evolutionary divergence. We then tested for effects of ecological factors on neophobia, including diet, sociality, habitat, and range, while accounting for phylogeny. We found that (i) species with more specialist diets were more neophobic than those with more generalist diets, providing support for the Neophobia Threshold Hypothesis; (ii) migratory species were also more neophobic than nonmigratory species, which supports the Dangerous Niche Hypothesis. Our study shows that the evolution of avian neophobia has been shaped by ecological drivers and demonstrates the potential of Big Team Science to advance our understanding of animal behavior.