  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.
