Reconstructing Human Faces from DNA: Competing Methodologies and the Quest for Replicability

Forensic DNA Phenotyping (FDP) technologies aim at reconstructing the face of a suspect from samples of DNA left at a crime scene. Law enforcement agencies employ FDP-generated “DNA Snapshots” of suspects in their criminal investigations, and share these with the media. Scholars expressed skepticism towards the “science” behind FDP. Clinical researchers argued that the methods upon which FDP are based on are hardly replicable and do not meet the scientific standards for validity and reliability (Hallgrimsson et al., 2014). Anthropologists pointed out that FDP-generated portrays are racially biased, and warned against the ethical issues related to their rapid diffusion (M’charek, 2017). Meanwhile, novel approaches to reconstructing faces from DNA samples keep emerging. In February 2018, an international team of physical anthropologists and computer engineers published on Nature Genetics a novel methodology that aims at addressing past criticism (Claes et al., 2018). Central to this novel methodology is the use of phenotypic and genotypic data from genome-wide association studies (GWAS), and of machine-learning algorithms for the calculation of facial phenotypes. Drawing on ethnographic research and document analysis from early 2000s to present days, this paper narrates the emergence of data-driven methodologies for DNA-based facial reconstruction, and examines the rationales behind their adoption as the new standard for replicable research on DNA-based facial reconstruction. Most importantly, the paper highlights the persistence of arbitrary choices made by the researchers in defying facial phenotypes over the years and throughout different methods, including novel data-driven approaches.