I compare two different arguments for the importance of bringing new voices into science: arguments for increasing the representation of women, and arguments for the inclusion of the public, or for “citizen science”. I suggest that in each case, diversifying science can improve the quality of scientific results in three distinct ways: epistemically, ethically, and politically. In the first two respects, the mechanisms are essentially the same. In the third respect, the mechanisms are importantly different. Though this might appear to (...) suggest a broad similarity between the cases, I show that the analysis reveals an important respect in which efforts to include the public are more complex. With citizen science programs, unlike with efforts to bring more women into science, the three types of improvement are often in conflict with one another: improvements along one dimension may come at a cost on another dimension, suggesting difficult trade-offs may need to be made. (shrink)

Several science studies scholars report instances of scientific “hype,” or sensationalized exaggeration, in journal articles, institutional press releases, and science journalism in a variety of fields (e.g., Caulfield and Condit 2012). Yet, how “hype” is being conceived varies. I will argue that hype is best understood as a particular kind of exaggeration, one that explicitly or implicitly exaggerates various positive aspects of science in ways that undermine the goals of science communication in a particular context. This account also makes clear (...) the ways that value judgments play a role in judgments of “hype,” which has implications for detecting and addressing this problem. (shrink)

Recent rival attempts in the philosophy of science to put forward a general theory of the properties that all (and only) natural kinds across the sciences possess may have proven to be futile. Instead, I develop a general methodological framework for how to philosophically study kinds. Any kind has to be investigated and articulated together with the human aims that motivate referring to this kind, where different kinds in the same scientific domain can answer to different concrete aims. My core (...) contention is that non-epistemic aims, including environmental, ethical, and political aims, matter as well. This is defended and illustrated based on several examples of kinds, with particular attention to the role of social-political aims: species, race, gender, as well as personality disorders and oppositional defiant disorder as psychiatric kinds. Such non-epistemic aims and values need not always be those personally favoured by scientists, but may have to reflect values that matter to relevant societal stakeholders. Despite the general agenda to study ‘kinds,’ I argue that philosophers should stop using the term ‘natural kinds,’ as this label obscures the relevance of humans interests and the way in which many kinds are based on contingent social processes subject to human responsibility. (shrink)

Philosophical work on science and values has come to engage with the concerns of society and of stakeholders affected by science and policy, leading to socially relevant philosophy of science and socially engaged philosophy of science. This special issue showcases instances of socially relevant philosophy of science, featuring contributions on a diversity of topics by Janet Kourany, Andrew Schroeder, Alison Wylie, Kristen Intemann, Joyce Havstad, Justin Biddle, Kevin Elliott, and Ingo Brigandt.

Recent scholarship in philosophy of science and technology has shown that scientific and technological decision making are laden with values, including values of a social, political, and/or ethical character. This paper examines the role of value judgments in the design of machine-learning systems generally and in recidivism-prediction algorithms specifically. Drawing on work on inductive and epistemic risk, the paper argues that ML systems are value laden in ways similar to human decision making, because the development and design of ML systems (...) requires human decisions that involve tradeoffs that reflect values. In many cases, these decisions have significant—and, in some cases, disparate—downstream impacts on human lives. After examining an influential court decision regarding the use of proprietary recidivism-prediction algorithms in criminal sentencing, Wisconsin v. Loomis, the paper provides three recommendations for the use of ML in penal systems. (shrink)