  For several decades, legal and scientific scholars have argued that
conclusions from forensic examinations should be supported by statistical data
and reported within a probabilistic framework. Multiple models have been
proposed to quantify the probative value of forensic evidence. Unfortunately,
several of these models rely on ad-hoc strategies that are not scientifically
sound. The opacity of the technical jargon used to present these models and
their results, and the complexity of the techniques involved make it very
difficult for the untrained user to separate the wheat from the chaff. This
series of papers is intended to help forensic scientists and lawyers recognise
limitations and issues in tools proposed to interpret the results of forensic
examinations. This paper focuses on tools that have been proposed to leverage
the use of similarity scores to assess the probative value of forensic
findings. We call this family of tools "score-based likelihood ratios". In this
paper, we present the fundamental concepts on which these tools are built, we
describe some specific members of this family of tools, and we explore their
convergence to the Bayes factor through an intuitive geometrical approach and
through simulations. Finally, we discuss their validation and their potential
usefulness as a decision-making tool in forensic science.
