Law enforcement use of genetic genealogy databases in criminal investigations: Nomenclature, definition and scope.

Although law enforcement use of commercial genetic genealogy databases has gained prominence since the arrest of the Golden State Killer in 2018, and it has been used in hundreds of cases in the United States and more recently in Europe and Australia, it does not have a standard nomenclature and scope. We analyzed the more common terms currently being used and propose a common nomenclature: investigative forensic genetic genealogy (iFGG). We define iFGG as the use by law enforcement of genetic genealogy combined with traditional genealogy to generate suspect investigational leads from forensic samples in criminal investigations. We describe iFGG as a proper subset of forensic genetic genealogy, that is, FGG as applied by law enforcement to criminal investigations; hence, investigative FGG or iFGG. We delineate its steps, compare and contrast it with other investigative techniques involving genetic evidence, and contextualize its use within criminal investigations. This characterization is a critical input to future studies regarding the legal status of iFGG and its implications on the right to genetic privacy.

Fair and equitable AI in biomedical research and healthcare: Social science perspectives.

Artificial intelligence (AI) offers opportunities but also challenges for biomedical research and healthcare. This position paper shares the results of the international conference "Fair medicine and AI" (online 3-5 March 2021). Scholars from science and technology studies (STS), gender studies, and ethics of science and technology formulated opportunities, challenges, and research and development desiderata for AI in healthcare. AI systems and solutions, which are being rapidly developed and applied, may have undesirable and unintended consequences including the risk of perpetuating health inequalities for marginalized groups. Socially robust development and implications of AI in healthcare require urgent investigation. There is a particular dearth of studies in human-AI interaction and how this may best be configured to dependably deliver safe, effective and equitable healthcare. To address these challenges, we need to establish diverse and interdisciplinary teams equipped to develop and apply medical AI in a fair, accountable and transparent manner. We formulate the importance of including social science perspectives in the development of intersectionally beneficent and equitable AI for biomedical research and healthcare, in part by strengthening AI health evaluation.