Although decision‐making algorithms are not new to medicine, the availability of vast stores of medical data, gains in computing power, and breakthroughs in machine learning are accelerating the pace of their development, expanding the range of questions they can address, and increasing their predictive power. In many cases, however, the most powerful machine learning techniques purchase diagnostic or predictive accuracy at the expense of our ability to access “the knowledge within the machine.” Without an explanation in terms of reasons or (...) a rationale for particular decisions in individual cases, some commentators regard ceding medical decision‐making to black box systems as contravening the profound moral responsibilities of clinicians. I argue, however, that opaque decisions are more common in medicine than critics realize. Moreover, as Aristotle noted over two millennia ago, when our knowledge of causal systems is incomplete and precarious—as it often is in medicine—the ability to explain how results are produced can be less important than the ability to produce such results and empirically verify their accuracy. (shrink)

Bioethicists frequently call for empirical researchers to engage participants and community members in their research, but don't themselves typically engage community members in their normative research. In this article, we describe an effort to include members of the public in normative discussions about the risks, potential benefits, and ethical responsibilities of social and behavioral genomics (SBG) research. We reflect on what might—and might not— be gained from engaging the public in normative scholarship and on lessons learned about public perspectives on (...) the risks and potential benefits of SBG research and the responsible conduct and communication of such research. We also provide procedural lessons for others in bioethics who are interested in engaging members of the public in their research. (shrink)

In the last third of the twentieth century, humanists and social scientists argued that attention to genetics would heighten already‐existing genetic determinism, which in turn would intensify negative social outcomes, especially sexism, racism, ableism, and harshness to criminals. They assumed that laypeople are at risk of becoming genetic essentialists. I will call this the “laypeople are genetic essentialists model.” This model has not accurately predicted psychosocial impacts of findings from genetics research. I will be arguing that the failure of the (...) model can be traced to its inability to recognize the complexity of laypeople’s attitudes; its incorrect theory of how beliefs, attitudes, and discourse function; and its blindness to how academics’ own interests can override the available evidence. More specifically, I suggest that the substantial data about laypeople’s deployment of genetics supports what I will call the “laypeople are strategic essentialists model” better than the “laypeople are genetic essentialists model.” The strategic essentialists model holds that people tend to store multiple categories, including multiple causal forces, that they deploy “strategically” to serve context‐dependent goals. It will be difficult for academics to reorient ourselves to model laypeople as sophisticated strategic essentialists rather than as naïve genetic essentialists. Perhaps a little shift, however, will be of value. (shrink)

Using mobile health research as an extended example, this article provides an overview of when the Common Rule “applies” to a variety of activities, what might be meant when one says that the Common Rule does or does not “apply,” the extent to which these different meanings of “apply” matter, and, when the Common Rule does apply, how it applies.

Fisher’s 1918 paper accomplished two distinct goals: unifying discrete Mendelian genetics with continuous biometric phenotypes and quantifying the variance components of variation in complex human characteristics. The former contributed to the foundation of modern quantitative genetics; the latter was adopted by social scientists interested in the pursuit of Galtonian nature-nurture questions about the biological and social origins of human behavior, especially human intelligence. This historical divergence has produced competing notions of the estimation of variance ratios referred to as heritability. Jay (...) Lush showed that they could be applied to selective breeding on the farm, while the early twin geneticists used them as a descriptive statistic to describe the degree of genetic determination in complex human traits. Here we trace the early history of the heritability coefficient now used by social scientists. (shrink)