Race is back with a vengeance. The role of genetics and genomics can hardly be underestimated in this resurfacing of race. While geneticists repeatedly assert that race and appearance have become irrelevant in their practice, social scientists have alerted us to what Troy Duster (2006) has called the “reinscription” of race at the molecular level. True, the molecularization of similarities and differences is ubiquitous, rendering DNA and data interchangeable (Kay 2000). Here, DNA and data are taken as archives that can be mined to bring to the surface features that otherwise would remain hidden. Yet I argue that what we are witnessing is not simply the molecularization of race, but the return of the phenotype and the biologization of physical appearance. To be sure, the phenotype is not a quality to be found in the body or on its surface. Rather it is a quality of the body. It is a material-semiotic assemblage involving diverse entities.

Forensic genetics is the field par excellence where the phenotype has come to play a key role. DNA found at a crime scene is conventionally used to include or exclude a suspect whose identity is known. Nowadays, DNA research is also used to infer the identity of the unknown suspect. It is aimed at producing clues about the appearance of the suspect and thus providing leads for the criminal investigation. Inferring characteristics such as sex, ancestry, hair, and eye color from the DNA should give a face to the unknown suspect.

While DNA phenotyping is largely a promissory science, with just a few physical markers in place, recently a U.S.-based company has claimed to be helping solve crime by producing a DNA photofit of the unknown suspect.

The DNA photofit of a presumed suspect conforms to the aesthetics of the portrait, a realistic rendering or snapshot of an individual and his face. The diagram as a whole alerts us to the fact that it is a patchwork, a composite. It generously invites us to attend to the issue of scale. We move up and down from the gene to the globe, from the individual to the population, from the molecular to the body surface, from face to race.

It is this issue of scale that I would like to pause with while thinking about the question of digital ontologies. For might it be that the ontology of the digital is constituted by its potential to move swiftly—as fast as light—between different scales, making and unmaking entities and producing an amazing level of complexity? Might it be that while digital technologies are often mobilized to make sense of and discover patterns in a complexity “out there,” scaling up and down may be a normative technology aimed at cleaning up the mess (the complexities produced by the digital itself) while seeming to produce a snapshot of reality as it really is?

Peter Claes, Harold Hill, and Mark Shriver (2014) have engaged in a study of faces on the basis of genetic markers and facial landmarks facilitated by 3D images. Their work examines three populations: U.S.-American, Brazilian, and Cape Verdean individuals between eighteen and forty years of age. Effectively, the three populations were genetically clustered according to facial forms associated with European and African ancestry. This is puzzling because their published work opens with a claim to move away from “simple scalar measures” (Claes, Hill, and Shriver 2014, 208), measures that have a long history in physical anthropology and its engagement in producing/knowing the racial type.

Traditionally, such physical complexity is simplified by simple scalar measurements [. . .]. However [. . .], a more impartial and systematic approach to modeling facial morphology is available and can facilitate both the gene discovery steps, as we recently showed, and DNA-based facial composite construction, as we show here. We first use genomic ancestry and sex to create a base-face, which is simply an average sex- and ancestry-matched face.

Subsequently, the effects of 24 individual SNPs [single nucleotide polymorphisms] that have been shown to have significant effects on facial variation are overlaid on the base-face forming the predicted-face in a process akin to a photomontage.

So here, already in the abstract, a huge complexity emerges. Analyses of the 3D images of 592 individuals have suggested forty-four principal components (landmarks) on the basis of which all facial variation can be explained, or so we learn a bit later in the article. Here, the technology, which has been trained to see facial morphology and difference through technologies of physical anthropology and craniofacial research, promises patterns that are invisible to eyes. The digital promises to uncover the real. But then, going from the surface to the molecular, population genetics is called upon to produce a “base-face.” Technically, this would be the average face compiled on the basis of 591 individuals (all subjects except for the subject whose facial shape is to be determined). But this analysis is not simply aimed at the individual. The 24 SNPs aimed at individualizing face are statistically accurate only when analyzed within homogeneous populations: hence, the populations of African and European ancestry. In other words, the “base-face” is the face of the population to which the individual subject allegedly belongs. According to genetic clustering based on ancestry markers, their faces are allocated to one of these populations. The forty-four landmarks can then be used to correlate and determine the base-face of each of these populations: an operation not very dissimilar to the making of racial types.

The samples for this study were collected in the United States, Brazil, and Cape Verde, but the data have taken us to Africa and Europe. The digital operation that has moved us up and down, in and out of the body is likened to a photomontage. However, whereas the montage tends to reveal to the viewer its process of making, the faces produced in the analyses and narration of the study data are naturalized. Faces and races, digital ontologies.

Acknowledgments

Research for this post was funded by the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007–2013), ERC grant agrement no. 339123.