My research focuses on the ethical, social and political impacts of emerging technologies. I explain how technology disrupts norms, values, and social practices. My work pays special attention to the ways in which technology can be developed to achieve justice for vulnerable populations such as pregnant women, children, and the elderly.
My primary research program stems from my dissertation, "Growing Pains: The Family in the Era of Technology," where I examine the family institution with a particular aim of determining the best social arrangements for bearing and rearing children in the context of emerging reproductive technologies. My second research program focuses on the ethical issues surrounding the design and development of artificial intelligence.
"Willing Mothers: Ectogenesis and the Role of Gestational Motherhood"
Journal of Medical Ethics (2020)
Ectogenesis (artificial womb technology) is currently being studied for the purpose of improving neonatal care. I contend that this technology ought to be pursued in order to address a more pressing problem: the rate of unintended pregnancies. However, ectogenesis threatens to disrupt the natural link between procreation and parenthood that is normally thought to generate rights and obligations for biological parents. I argue that there remains only one potentially viable account of parenthood: the voluntarist account. The problem is that this account mistakenly presumes a patriarchal divide between procreation and parenthood. By reframing procreation and parenthood from a feminist perspective, I argue that gestational motherhood is a robust moral obligation that ought to be voluntarily undertaken. If this were the case, all gestational mothers would be, by definition, willing mothers. To make this happen, I argue that ectogenesis technology must be a widely-available reproductive option.
In July 2020 (new date TBD), I will be completing a one-month visiting research residency at the Brocher Foundation in Switzerland. The goal of this non-profit foundation is to encourage interdisciplinary research into medical research and biotechnologies. During my time as a resident, I aim to collaborate with other experts as well as international organizations and NGOs based in Geneva, including the WHO, on issues concerning the social implications of emerging assisted-reproductive technologies (ARTs).
Investigate the injustices associated with the hegemony of the natural nuclear family schema.
Investigate how procreation fails to be considered an importantly gendered concept
Question how emerging ARTs may function to serve, or complicate, goals to make procreation and parenthood more gender egalitarian
Analyze how ARTs may negatively affect the perceived value of adoptive parent-child relationships
WORKS IN PROGRESS
Widening Access to Applied Machine Learning with TinyML
Co-authored with Vijay Janapa Reddi, Brian Plancher, et al.
Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this paper, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML both leverages low-cost and globally accessible hardware, and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia (Harvard University) and industry (Google) produced a four-part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for learners from a global variety of backgrounds. It introduces pupils to real-world applications, ML algorithms, data-set engineering, and the ethical considerations of these technologies via hands-on programming and deployment of TinyML applications in both the cloud and their own microcontrollers. To facilitate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project competition. We also released the course materials publicly, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies.