When Not to Ask: A Defense of Choice-Masking Nudges in Medical Research.

In this article, we examine the legality and ethics of a controversial but widespread practice in clinical research: choice-masking nudges. A choice-masking nudge (CMN) exists when a research team explicitly obscures a meaningful choice from participants by presenting a default decision as the standard way forward. Even though an easy-to-use opt-out mechanism is available for participants who independently express concerns with the standard default, the fact that a default has been pre-selected is not made obvious to research participants. To opt out of the nudge, a participant must overtly request non-standard treatment. We argue that use of such nudges in medical research can be justified by their individual, collective, and social benefits, provided that they respect autonomy and satisfy our four further acceptability conditions. The structure of this Article is as follows. In Part II, we describe three controversial cases of CMNs in medical research. In Part III, we provide background on nudging and explain how our proposed CMNs fit into the existing literature on nudging and libertarian paternalism. In Part IV, we explain how the reasonable person standard as employed by United States research regulations can be used to support CMNs. In Part IV, we anticipate some of the strongest objections to CMNs by explaining how CMNs are compatible with a wide range of plausible accounts of autonomy. Finally, in Part VI, we discuss four additional core considerations an acceptable CMN must meet: legitimate policy goals; benefits outweighing harms; burdens distributed fairly; and absence of ethically superior feasible alternatives. We also analyze the three existing controversies explored in Part II and show how each would benefit from the conceptual clarity offered by our analytic framework. Medical research is complicated and can be difficult for participants to understand; thoughtfully designed CMNs can play an important role in gently guiding large numbers of research participants toward decision outcomes that really are best for them and their communities.