Narrative Bayesian Networks for Evaluating Forensic Fibre Evidence
Organized by: International Forensic Scientist Awards
Website: forensicscientist.org
16th Edition of Forensic Scientist Awards 28-29 November 2025 | Agra, India
Evaluating forensic fibre evidence at the activity level has always been a complex task. Traditional methods often struggle to incorporate contextual information, competing explanations, and the uncertainties inherent in trace evidence. To address these challenges, narrative Bayesian Networks (BNs) offer a structured and transparent framework that enhances the clarity and reliability of forensic interpretations.
Narrative BNs combine storytelling with probabilistic modelling. Instead of starting with equations or isolated variables, the process begins by outlining the narrative of what might have happened—such as how a fibre was transferred, how long it persisted, and whether alternative activities could account for its presence. These narrative elements are then translated into interconnected BN nodes that represent evidence, actions, and contextual factors.
This methodology enables forensic scientists to evaluate fibre findings under activity-level propositions, such as whether contact occurred or if a fibre could have been transferred during an unrelated event. By quantifying the likelihood of different scenarios, BNs support more robust likelihood ratio assessments and promote transparency in reporting.
The approach also enhances communication with courts and investigators. Since narrative BNs visually show how assumptions and evidence interact, they help explain complex reasoning in a clear and structured manner.
Overall, narrative Bayesian Networks provide a powerful tool for strengthening forensic interpretations, reducing ambiguity, and improving the evidential value of fibre analysis in real-world casework.
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