Marion Boulicault

Ranging from miniaturized biological robots to organoids, multi-cellular engineered living systems (M-CELS) pose complex ethical and societal challenges. Some of these challenges, such as how to best distribute risks and benefits, are likely to arise in the development of any new technology. Other challenges arise specifically because of the particular characteristics of M-CELS. For example, as an engineered living system becomes increasingly complex, it may provoke societal debate about its moral considerability, perhaps necessitating protection from harm or recognition of positive moral and legal rights, particularly if derived from cells of human origin. The use of emergence-based principles in M-CELS development may also create unique challenges, making the technology difficult to fully control or predict in the laboratory as well as in applied medical or environmental settings. In response to these challenges, we argue that the M-CELS community has an obligation to systematically address the ethical and societal aspects of research and to seek input from and accountability to a broad range of stakeholders and publics. As a newly developing field, M-CELS has a significant opportunity to integrate ethically responsible norms and standards into its research and development practices from the start. With the aim of seizing this opportunity, we identify two general kinds of salient ethical issues arising from M-CELS research, and then present a set of commitments to and strategies for addressing these issues. If adopted, these commitments and strategies would help define M-CELS as not only an innovative field, but also as a model for responsible research and engineering.

Machine learning offers new possibilities for developing more precise diagnostics and treatments, but the increasing use of sex stratification in precision medicine algorithms raises concerns. Using Alzheimer's disease (AD) research as an example in which machine learning approaches are applied to a heterogenous, socially patterned disease, this paper examines how the move toward sex-specific “pink” and “blue” algorithms reinforces biological sex essentialist assumptions and their attendant harms. We analyze three examples of sex-stratified algorithmic approaches in AD research, and identify three interacting processes-effacing contested knowledge, obscuring social factors, and ossifying binary sex categories-that can occur when binary sex variables are incorporated into predictive models. These case studies demonstrate that even in models intended to be causally agnostic, sex categories are likely to be interpreted as decontextualized, self-evident health determinants in a manner that can imply causality of biological sex. We call for establishing ethical norms and empirical standards for including gender/sex variables in precision medicine algorithms to avoid perpetuating crude ontologies of sex and gender that undermine both scientific validity and health justice.

To explain observed disparities in health outcomes between men and women, sex essentialist approaches assign causal primacy to sex-related biology. In this essay, we present three case studies to illustrate how sex essentialism can distort human biomedical research and distill three maxims for countering this distortion: (1) engage in responsible citation practices; (2) generate and weigh alternative hypotheses for apparent observations of sex differences; (3) take care in constructing the appropriate denominator when making sex comparisons. We offer these maxims as broadly applicable standards of evidence to guide biomedical research that includes analysis of potential sex differences, as well as to support Institutional Review Boards (IRBs), funders, publishers, and peer reviewers in evaluating sex difference findings. If widely applied, these maxims would substantially improve the rigor, precision, and utility of the knowledge base of sex and gender science.

Meet Tega. Blue, fluffy, and AI-enabled, Tega is a relational robot: a robot designed to form relationships with humans. Created to aid in early childhood education, Tega talks with children, plays educational games with them, solves puzzles, and helps in creative activities like making up stories and drawing. Children are drawn to Tega, describing him as a friend, and attributing thoughts and feelings to him ("he's kind," "if you just left him here and nobody came to play with him, he might be sad"). Scholars and members of the public alike have raised the alarm about relational robots, worrying that the relationships that people, and especially children, form with such robots are objectionably inauthentic. We, members of an interdisciplinary team that developed and studies Tega, make this inauthenticity worry precise and offer practical recommendations for addressing it. We distinguish two kinds of (in)authenticity -- inauthenticity as unreality, and inauthenticity as deception -- arguing that neither is just what others have thought it is. With our distinction in hand, we argue that the authenticity concern can be met only through co-design methods -- methods that give stakeholders of all kinds (e.g. parents, educators, and children themselves) a genuine say in how relational robots are built.

Family members can provide crucial support to individuals participating in clinical trials. In research on the “newest frontier” of Deep Brain Stimulation (DBS)—the use of DBS for psychiatric conditions—family member support is frequently listed as a criterion for trial enrollment. Despite the significance of family members, qualitative ethics research on DBS for psychiatric conditions has focused almost exclusively on the perspectives and experiences of DBS recipients. This qualitative study is one of the first to include both DBS recipients and their family members as interview participants. Using dyadic thematic analysis—an approach that takes both the individuals and the relationship as units of analyses—this study analyzes the complex ways in which family relationships can affect DBS trial participation, and how DBS trial participation in turn influences family relationships. Based on these findings, we propose ways to improve study designs to better take family relationships into account, and better support family members in taking on the complex, essential roles that they play in DBS trials for psychiatric conditions.

Early in the COVID-19 pandemic, researchers reported a surprising trend in disease outcomes: men were more likely to require hospitalization and die from COVID-19 than women. Researchers looked to sex-linked biology to explain these disparities, hypothesizing innate sex differences in immune function, suggesting the use of estrogens or androgen-suppressants as therapy, and even pushing for sex-specific vaccine strategies. Leading bioethicists like Dr. Ezekiel Emanuel at the University of Pennsylvania recently described the sex disparity in COVID-19 outcomes as "the unsolved mystery" of the...

Background Inequities in COVID-19 outcomes in the USA have been clearly documented for sex and race: men are dying at higher rates than women, and Black individuals are dying at higher rates than white individuals. Unexplored, however, is how sex and race interact in COVID-19 outcomes. Objective Use available data to characterize COVID-19 mortality rates within and between race and sex strata in two US states, with the aim of understanding how apparent sex disparities in COVID-19 deaths vary across race. Design and Participants This observational study uses COVID-19 mortality data through September 21, 2020, from Georgia (GA) and Michigan (MI). Main Measures We calculate age-specific rates for each sex-race-age stratum, and age-standardized rates for each race-sex stratum. We investigate the sex disparity within race groups and the race disparity within sex groups using age-standardized rate ratios, and rate differences. Key Results Within race groups, men have a higher COVID-19 mortality rate than women. Black men have the highest rate of all race-sex groups (in MI: 254.6, deaths per 100,000, 95% CI: 241.1–268.2, in GA:128.5, 95% CI: 121.0-135.9). In MI, the COVID-19 mortality rate for Black women (147.1, 95% CI: 138.7–155.4) is higher than the rate for white men (39.1, 95% CI: 37.3–40.9), white women (29.7, 95% CI: 28.3–31.0), and Asian/Pacific Islander men and women. COVID-19 mortality rates in GA followed the same pattern. In MI, the male:female mortality rate ratio among Black individuals is 1.7 (1.5–2.0) while the rate ratio among White individuals is only 1.3 (1.2–1.5). Conclusion While overall, men have higher COVID-19 mortality rates than women, our findings show that this sex disparity does not hold across racial groups. This demonstrates the limitations of unidimensional reporting and analyses and highlights the ways that race and gender intersect to shape COVID-19 outcomes.

Is the feminist project to bring about parity for women and men in traditionally male fields doomed? Recent headlines trumpet that "The more gender equality, the fewer women in STEM." The American Enterprise Institute proposes that it is futile to fund efforts to increase women in STEM fields, given that, “as paradoxical and counter-intuitive as it seems, female underrepresentation in STEM may actually be the result of the great advances in female empowerment, progress, and advancement that have taken place in recent decades, and not the result of systemic gender discrimination.” Meanwhile, men's rights activists exalt new findings as evidence that women just naturally don’t prefer science. These bold claims stem from the “Gender Equality Paradox” hypothesis, which is simply this: Some of the countries with the highest gender equity scores on internationally-comparable indices also show the largest average sex differences in prefrences for STEM careers. In other words, in countries with more gender equality, like Norway and Sweden, fewer women choose to enter STEM fields than in countries like Algeria or Turkey. Over the past year, the GenderSci Lab has been engaged in a critical analysis of the “Gender Equality Paradox” hypothesis and its accompanying assumptions and implications. In this paper, we share our reanalysis of some of the data used by Stoet and Geary (2018) and argue that their findings are likely spurious.

In “Analyzing COVID-19 Sex Difference Claims: The Harvard GenderSci Lab,” Marion Boulicault and Sarah Richardson summarize some of the groundbreaking work that they’re doing at the Harvard GenderSci Lab. Since March 2020, their lab has been analyzing, interrogating, and critiquing sex essentialist explanations of COVID-19 outcome disparities that are fairly ubiquitous in news media. Using interdisciplinary tools from feminist philosophy, science studies, and critical public health, they work collaboratively with two goals: (i) to critically examine COVID-19 sex difference research and (ii) to explore and elevate the role of social variables in driving biological disparities. They argue that in public health research, media, and messaging, data on sex disparities must be contextualized both to avoid reinforcing harmful sex essentialist assumptions and also to help the public understand the complex ways in which social factors influence these patterns. They argue that within the context of COVID-19, doing so can both clarify risks and save lives. Their contribution to this issue describes their methods and shares some of their findings.

The past 50 years have seen heated debate in the reproductive sciences about global trends in human sperm count. In 2017, Levine and colleagues published the largest and most methodologically rigorous meta-regression analysis to date and reported that average total sperm concentration among men from ‘Western’ countries has decreased by 59.3% since 1973, with no sign of halting. These results reverberated in the scientific community and in public discussions about men and masculinity in the modern world, in part because of scientists’ public-facing claims about the societal implications of the decline of male fertility. We find that existing research follows a set of implicit and explicit assumptions about how to measure and interpret sperm counts, which collectively form what we term the Sperm Count Decline hypothesis (SCD). Using the study by Levine and colleagues, we identify weaknesses and inconsistencies in the SCD, and propose an alternative framework to guide research on sperm count trends: the Sperm Count Biovariability hypothesis (SCB). SCB asserts that sperm count varies within a wide range, much of which can be considered non-pathological and species-typical. Knowledge about the relationship between individual and population sperm count and life-historical and ecological factors is critical to interpreting trends in average sperm counts and their relationships to health and fertility.

What makes science trustworthy to the public? This chapter examines one proposed answer: the trustworthiness of science is based at least in part on its independence from the idiosyncratic values, interests, and ideas of individual scientists. That is, science is trustworthy to the extent that following the scientific process would result in the same conclusions, regardless of the particular scientists involved. We analyze this "idiosyncrasy-free ideal" for science by looking at philosophical debates about inductive risk, focusing on two recent proposals which offer different methods of avoiding idiosyncrasy: the high epistemic standards proposal and the democratic values proposal.