Double Loop Podcast

Glenn Langenburg returns from international travels (just in time) and brings stories from distant lands of England and Minnesota. As the discussion turns to conclusions, Eric Ray brings an update from the OSAC Friction Ridge Subcommittee. Check the NIST OSAC website in the near future for updated versions of Friction Ridge Subcommittee proposed standards.

Eric interviews Glenn about how he got started in latent prints and his favorite time teaching. The guys then discuss regional differences in the latent print field including terms, definitions, and powder color.

Eric and Glenn respond to the recent allegations that a computer science undergraduate at Columbia University, using Artificial Intelligence, has “proven that fingerprints aren’t unique” or at least...that’s how the media is mischaracterizing a new published paper by Guo, et al. The guys dissect the actual publication (“Unveiling intra-person fingerprint similarity via deep contrastive learning” in Science Advances, 2024 by Gabe Guo, et al.). They state very clearly what the paper actually does show, which is a far cry from the headlines and even public dissemination originating from Columbia University and the author. The guys talk about some of the important limitations of the study and how limited the application is to real forensic investigations. They then explore some of the media and social media outlets that have clearly misunderstood this paper and seem to have little understanding of forensic science. Finally, Eric and Glenn look at some quotes and comments from knowledgeable sources who also have recognized the flaws in the paper, the authors’ exaggerations, and lack of understanding of the value of their findings. Gabe Guo et al. ,Unveiling intra-person fingerprint similarity via deep contrastive learning.Sci. Adv.10, eadi0329(2024). DOI:10.1126/sciadv.adi0329 https://www.science.org/doi/10.1126/sciadv.adi0329 https://www.engineering.columbia.edu/news/ai-discovers-not-every-fingerprint-unique https://for-sci-law.blogspot.com/ https://www.cnn.com/2024/01/12/world/fingerprints-ai-based-study-scn/index.html

Eric and Glenn interview returning guest, and newly minted PhD, Dr. Henry Swofford on his recent paper, with co-author Prof. Christophe Champod: “Probabilistic reporting and algorithms in forensic science: Stakeholder perspectives within the American criminal justice system” (For Sci Intl Synergy 2020; https://doi.org/10.1016/j.fsisyn.2022.100220). The new article discusses interview responses from judges, prosecutors, defense attorneys, scholars, and forensic laboratory managers regarding their perspective on how to validate, implement, litigate, and communicate probabilistic tools and algorithms in American courts. They discuss different quotes and perspectives from the paper and relate that to their own experiences and observations. Henry also makes an important reference to a companion paper, that provides concrete recommendations and a strategy towards implementation by practitioners: “Implementation of algorithms in pattern and impression evidence: A responsible and practical roadmap” (FSI Synergy 2021; https://doi.org/10.1016/j.fsisyn.2021.100142).

Today, Eric and Glenn FINALLY review the Stephan Cowans case, a case of an erroneous identification from Boston where a fingerprint identification was critical evidence used to convict Cowans in 1998. This case is one of the few Innocence Project cases where there was an erroneous fingerprint identification but DNA was able to exonerate Cowans in 2004 (and the true source of the latent print was later identified). Eric and Glenn go through the major elements of the case and the subsequent review of the Boston Police Department Latent Print Unit by an external consultant. At the end, they discuss the critical improvements and advancements made in the wake of the error and why these cases are still important today to review and remember.