Yasmin Haddad

In the past decades, there has been a significant shift in the labels used to describe human populations in large-scale genomic studies. Such labels are known as population descriptors. The once prevalent descriptor “race” has progressively given way to the concept of “ancestry”. Ancestry is measured through genetic similarity: individuals who are genetically similar are considered to have shared ancestry. The use of rigorous techniques – including measurement procedures - to assess “ancestry” give it a cloak of objectivity, which in turn justifies the widespread adoption of the concept. However, this objectivity might only be apparent once we understand the challenges to measuring ancestry. This paper contributes to the literature by bridging the previously disconnected fields of philosophy of measurement and human population genetics. To do so, I draw on model-based accounts of measurement to describe practices of measuring ancestry. I then outline two measurement challenges in clustering humans based on genetic ancestry. The first is the challenge of coordination which describes the circularity between theoretical assumptions and measurement outcomes, also known as the theory-ladenness of observation. The second is the issue of biases, which include the problems of sampling biases and the issue of proxy variables. Finally, I outline potential solutions to address these challenges.

Clustering humans based on their genetic ancestry is a common practice in human genomics. Genetically similar populations can be seen as statistical constructs that are labeled by population descriptors such as “race,” “ethnicity,” and “genetic ancestry.” Recently, there has been a shift towards replacing the descriptor “race” with “genetic ancestry” because the latter is considered more objective. A descriptor is deemed objective if it adequately captures an underlying feature of the biological world, such as genetic similarities or differences between human sub-populations. However, claims of objectivity do not sufficiently explain the rationale for the choice and use of population descriptors such as “ancestry.” This paper proposes an axiological approach to capture the choice and use of population descriptors in human genomics, by showing that the population descriptor “ancestry” is value-laden and that there is a legitimate role for values in the choice and use of population descriptors in genomics.

The role of reciprocal causation in Extended Evolutionary Synthesis (EES) is controversial. Proponents of EES argue that reciprocal causation is a key innovation, underpinning the necessity of EES. Conversely, critics of the EES maintain that Standard Evolutionary Theory (SET) adequately encompasses the concept of reciprocal causation, challenging the need for EES. This skepticism is rooted in two primary critiques. First, the mischaracterization of causal dynamics within SET by EES advocates leads to a misrepresentation of SET. Second, the oversight of how SET incorporates and acknowledges instances of reciprocal causation leads to claims about the empirical inaptness of SET. As a result, the debate has reached an impasse, with limited progress towards a constructive examination of reciprocal causation’s significance to evolutionary explanations. This paper introduces the scope argument, which examines reciprocal causation through timescales and grain of explanations. This approach revitalizes the debate in two ways. First, reframing the debate in terms of scope clarifies the role of reciprocal causation by allowing research programs to specify targets of explanation. Second, the elements of scope (timescales and grain) elucidate the epistemic advantage of reciprocal causation in the respective research programs in question.

The concept of downward causation is frequently used in an explanatory capacity in biology to account for certain regularities and processes. Some philosophers, however, argue that downward causation is metaphysically incoherent, providing three main objections. Underlying these objections is the assumption that entities are connected by compositional hierarchies of levels of organization. In this paper, I introduce the notions of weak and strong compositional relations using examples from evolutionary developmental biology. I argue that downward causation becomes unproblematic if we use features of interventionist theories of causation to explain the causal relations between levels. I show that an interventionist account of downward causation successfully responds to the three central objections to downward causation in the philosophical literature and I clarify the explanatory usefulness of the concept in biology. As such, this paper provides an epistemic solution to demystify downward causation in the context of scientific practice. The solution proposed is compatible with how biologists seem to use a concept of downward causation fruitfully in their work.

In this paper, I provide an epistemic evaluation of the harms that result from the widespread marketing of direct-to-consumer (DTC) genetic tests. While genetic tests are a valuable accessory diagnostic tool when ordered by a medical practitioner, there are different implications when they are sold directly to consumers. I aim to show that there are both epistemic and non-epistemic harms associated with the widespread commoditization of DTC genetic tests. I argue that the epistemic harms produced by DTC genetic tests have been disregarded in discussions on the topic. Drawing on the notion of contributory epistemic injustices, I highlight two pertinent epistemic harms: (1) a failure to uptake an individual’s articulations about their identity and (2) the presiding reductionist framework dismisses useful hermeneutical resources. I then propose ways to mitigate these harms.