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Abstract Recent studies have shown that recidivism scoring algorithms like COMPAS have significant racial bias: Black defendants are roughly twice as likely as white defendants to be mistakenly classified as medium- or high-risk. This has led some to call for abolishing COMPAS. But many others have argued that algorithms should instead be given access to a defendant's race, which, perhaps counterintuitively, is likely to improve outcomes. This approach can involve either establishing race-sensitive risk thresholds, or distinct racial ‘tracks’. Is there a moral difference between these two approaches? We first consider Deborah Hellman's view that the use of distinct racial tracks (but not distinct thresholds) does not constitute disparate treatment since the effects on individuals are indirect and does not rely on a racial generalization. We argue that this is mistaken: the use of different racial tracks seems both to have direct effects on and to rely on a racial generalization. We then offer an alternative understanding of the distinction between these two approaches—namely, that the use of different cut points is to the counterfactual comparative disadvantage, ex ante, of all white defendants, while the use of different racial tracks can in principle be to the advantage of all groups, though some defendants in both groups will fare worse. Does this mean that the use of cut points is impermissible? Ultimately, we argue, while there are reasons to be skeptical of the use of distinct cut points, it is an open question whether these reasons suffice to make a difference to their moral permissibility. 

Abstract Biological introductions are unintended “natural experiments” that provide unique insights into evolutionary processes. Invasive phytophagous insects are of particular interest to evolutionary biologists studying adaptation, as introductions often require rapid adaptation to novel host plants. However, adaptive potential of invasive populations may be limited by reduced genetic diversity—a problem known as the “genetic paradox of invasions”. One potential solution to this paradox is if there are multiple invasive waves that bolster genetic variation in invasive populations. Evaluating this hypothesis requires characterizing genetic variation and population structure in the invaded range. To this end, we assemble a reference genome and describe patterns of genetic variation in the introduced white pine sawfly, Diprion similis. This species was introduced to North America in 1914, where it has rapidly colonized the thin-needled eastern white pine (Pinus strobus), making it an ideal invasion system for studying adaptation to novel environments. To evaluate evidence of multiple introductions, we generated whole-genome resequencing data for 64 D. similis females sampled across the North American range. Both model-based and model-free clustering analyses supported a single population for North American D. similis. Within this population, we found evidence of isolation-by-distance and a pattern of declining heterozygosity with distance from the hypothesized introduction site. Together, these results support a single-introduction event. We consider implications of these findings for the genetic paradox of invasion and discuss priorities for future research in D. similis, a promising model system for invasion biology. 

Abstract Pine sawflies (Hymenoptera: Diprionidae) are eruptive herbivores found throughout eastern North America. The Diprionidae family, which contains at least 140 species, constitutes the most persistent threat to conifers as population outbreaks can cause widespread defoliation. Because some species are more prone to large, destructive outbreaks than others, species identification is critical to effective management. Although existing taxonomic keys are primarily based on internal adult morphology, substantial variation among species in larval color traits, geographic location, overwintering strategy, host plant, and egg patterns can be diagnostic at the species level. Here, we focus on the Pinaceae-feeding subfamily Diprioninae, of which there are 25 species in eastern North America. We describe the general biology, life cycle, and host-use ecology of Diprioninae, with an emphasis on the variation among these traits within this subfamily. In addition, we provide tools for species identification, including a taxonomic key that utilizes external diagnostic characteristics. Finally, we discuss available management strategies. 

Abstract A common criticism of the use of algorithms in criminal justice is that algorithms and their determinations are in some sense “opaque”—that is, difficult or impossible to understand, whether because of their complexity or because of intellectual property protections. Scholars have noted some key problems with opacity, including that opacity can mask unfair treatment and threaten public accountability. In this paper, we explore a different but related concern with algorithmic opacity, which centers on the role of public trust in grounding the legitimacy of criminal justice institutions. We argue that algorithmic opacity threatens the trustworthiness of criminal justice institutions, which in turn threatens their legitimacy. We first offer an account of institutional trustworthiness before showing how opacity threatens to undermine an institution's trustworthiness. We then explore how threats to trustworthiness affect institutional legitimacy. Finally, we offer some policy recommendations to mitigate the threat to trustworthiness posed by the opacity problem. 

This paper synthesizes scholarship from several academic disciplines to identify and analyze five major ethical challenges facing data-driven policing. Because the term “data-driven policing” encompasses a broad swath of technologies, we first outline several data-driven policing initiatives currently in use in the United States. We then lay out the five ethical challenges. Certain of these challenges have received considerable attention already, while others have been largely overlooked. In many cases, the challenges have been articulated in the context of related discussions, but their distinctively ethical dimensions have not been explored in much detail. Our goal here is to articulate and clarify these ethical challenges, while also highlighting areas where these issues intersect and overlap. Ultimately, responsible data-driven policing requires collaboration between communities, academics, technology developers, police departments, and policy makers to confront and address these challenges. And as we will see, it may also require critically reexamining the role and value of police in society.