Artificial intelligence enables scale, consistency, and rigor in forensic identity inference.

Clinical Snapshot

Key Findings

• Effect Size: Not applicable - no quantitative analysis

• Primary Outcome: Conceptual framework for AI-enabled forensic identity inference systems

• Nnt Or Sensitivity: Not applicable - conceptual review only

• Confidence Interval: Not provided

Clinical Application

Requires significant technological infrastructure and validation studies before implementation Would need alignment with Australian forensic standards, privacy laws, and validation by National Institute of Forensic Science (NIFS). Implementation would require coordination with state police forensic services and consideration of Australian privacy legislation. Forensic laboratories conducting DNA analysis and genetic genealogy investigations

Abstract

Advances in forensic genomics, which include massively parallel sequencing, dense single nucleotide polymorphism testing, and forensic genetic genealogy (FGG), have greatly expanded the range of cases in which DNA evidence can generate investigative leads. As a result, the primary limitations in modern forensic DNA analysis are no longer analytical sensitivity or marker availability, but the ability to reason consistently, transparently, and at scale over increasingly complex genetic, genealogical, and contextual information. Current forensic workflows remain predominantly human-centered, relying on manual reasoning that is difficult to standardize, reproduce, document, or scale across growing case inventories. The potential of artificial intelligence (AI) is described as an enabling layer for forensic identity inference, defined here as computational decision-support systems that structure, prioritize, and document reasoning over genetic associations, genealogical structures, and investigative context during identity hypothesis development. While formal statistical inference quantifies evidentiary weight, identity inference governs how evidence is explored, combined, and acted upon during investigations. AI-assisted systems can augment, but not replace, expert judgment by supporting scalable prioritization, relational reasoning, and systematic documentation of analytical decisions, while reducing bias. Properly designed AI-enabled systems offer a path to sustainably scaling FGG while supporting scientific rigor, accountability, and public trust.