To learn from the past, we analyse 1,088 "computer as a target" judgements for evidential reasoning by extracting four case elements: decision, intent, fact, and evidence. Analysing the decision element is essential for studying the scale of sentence severity for cross-jurisdictional comparisons. Examining the intent element can facilitate future risk assessment. Analysing the fact element can enhance an organization's capability of analysing criminal activities for future offender profiling. Examining the evidence used against a defendant from previous judgements can facilitate the preparation of evidence for upcoming legal disclosure. Follow the concepts of argumentation diagrams, we develop an automatic judgement summarizing system to enhance the accessibility of judgements and avoid repeating past mistakes. Inspired by the feasibility of extracting legal knowledge for argument construction and employing grounds of inadmissibility for probability assessment, we conduct evidential reasoning of kernel traces for forensic readiness. We integrate the narrative methods from attack graphs/languages for preventing confirmation bias, the argumentative methods from argumentation diagrams for constructing legal arguments, and the probabilistic methods from Bayesian networks for comparing hypotheses.
We identify 74 generic, reusable technical requirements based on the GDPR that can be applied to software products which process personal data. The requirements can be traced to corresponding articles and recitals of the GDPR and fulfill the key principles of lawfulness and transparency. Therefore, we present an approach to requirements engineering with regard to developing legally compliant software that satisfies the principles of privacy by design, privacy by default as well as security by design.
