Uniting Machine Intelligence, Brain and Behavioural Sciences to Assist Criminal Justice (2207.01511v2)

Abstract: I discuss here three important roles where machine intelligence, brain and behaviour studies together may facilitate criminal law. First, predictive modelling using brain and behaviour data may support legal investigations by predicting categorical, continuous, and longitudinal legal outcomes of interests related to brain injury and mental illnesses. Second, psychological, psychiatric, and behavioural studies supported by machine learning algorithms may help predict human behaviour and actions, such as lies, biases, and visits to crime scenes. Third, machine learning models have been used to predict recidivism using clinical and criminal data whereas brain decoding is beginning to uncover one's thoughts and intentions based on brain imaging data. Having dispensed with achievements and promises, I examine concerns regarding the accuracy, reliability, and reproducibility of the brain- and behaviour-based assessments in criminal law, as well as questions regarding data possession, ethics, free will (and automatism), privacy, and security. Further, I will discuss issues related to predictability vs. explainability, population-level prediction vs. personalised prediction, and predicting future actions, and outline three potential scenarios where brain and behaviour data may be used as court evidence. Taken together, brain and behaviour decoding in legal exploration and decision-making at present is promising but primitive. The derived evidence is limited and should not be used to generate definitive conclusions, although it can be potentially used in addition, or parallel, to existing evidence. Finally, I suggest that there needs to be (more precise) definitions and regulations regarding when and when not brain and behaviour data can be used in a predictive manner in legal cases.