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Improving the interpretation of forensic evidence in an investigative and legal setting.
The current issues that are faced by the forensic science community, which were identified by the National Academy of Sciences in the US and the Law Commission in the UK, are related to the interpretation of forensic evidence in the criminal case context. One of the main reasons for this is that the interpretation of forensic evidence is a key challenge for many legal professionals, which means that the use of the evidence is not used in its full potential.
Additionally, the UK Forensic Science Special Interest Group stated that using models and algorithms in forensic science should be one of the main points of development. One of these models, Bayesian networks, will be utilised within this project. The aim is to provide new insights into how Bayesian networks can improve the interpretation and evaluation of evidence, and to close the gap between probabilistic scientific analyses and the reasoning of legal professionals. The research that I will be undertaking addresses the current issues by providing a robust and general framework for the interpretation of evidence which considers available evidence, hypotheses and uncertainties. This framework will be adaptable to specific cases and improved by incorporating the results of experimental studies, expert knowledge, databases and information from published studies.
Ultimately, this will lead to a solid and robust framework which guides researchers, forensic scientists and legal professionals and minimizes the possibility of wrongful interpretations, miscommunication and false convictions, based on hard forensic evidence.