After developing a good enough mutual understanding of the core concepts of law and computer science, the second step will be to obtain a similar mutual understanding of the generative grammar that informs the practices of law and legal theory on the one hand and those of the relevant domains within computer science, notably cryptography and machine learning.
On the side of law, this will entail an understanding of how lawyers research and determine positive law in relation to a specified problem, and how legal theorists frame the objectives and the operations of law. On the side of computer science, this will entail an understanding of how cryptographers develop solutions to problems of digital security, and of how machine learners develop their research design.
Core to these grammars are the assumptions they embody and the implications these have for the normative orders that hold together local, national and international jurisdictions, societies and communities.
The listings below are tentative, exploratory and merely an indication of the direction of the research - they will be developed in more detail in research papers, articles, books, seminars and conferences which will be announced and referenced elsewhere on the site.
Grammars of law and legal theory
Aims of the law: justice (distributive, corrective); legal certainty (positivity, equality); purposiveness (instrumentality)
Deciding positive law: role of the legislator, administration, and courts
Institutions of positive law: legislator, administration, courts; practicing lawyers; public prosecutors; notary publics; public bodies; civil registries
Domain-specificity in law: public law, private law, criminal law, administrative law, environmental law, ambient law, data protection law, cyberlaw
Role of the constitution: written, unwritten; in relation to e.g. international human rights
Jurisdiction: national; international; supranational; transnational?
International law: monistic relationship with national law; dualistic relationship with national law
Legal norms: rules and/or principles and policies; exclusionary rules; primary and secondary rules; performative character of legal rules
Methods of reasoning: Analogous reasoning; a contrario reasoning
Interpretation: Systematic interpretation; grammatical interpretation; historical interpretation; teleological interpretation; extensive interpretation; restrictive interpretation; consistency of interpretation; integrity of interpretation
Grammars of cryptography and machine learning
The history and development of digital security: Shannon concept of information (compression); Turing concept of information (decoding); CIA; threats; vulnerabilities
Approaches to resilience: methods of attack modelling; methods of malware detection; methods of exploiting vulnerabilities
Distributed ledger technologies: the role of miners; claims of immutability, distribution, trust; off-chain data input; the role of consensus; proof of work; proof of stake; private and public blockchains; permissioned and permissionless blockchains; transparency, hashing and encryption; privacy, hashing and encryption; self-executing code; issues of truth, correctness, compliance, contestation
Cybernetics and Artificial Intelligence: Wiener concept of information (feedback, control); McCarthy concept of intelligence (beliefs); Searle’s critique of AI (consciousness)
Maching learning research design: the role of induction; Bayesian inferences; ML as search (for mathematical target function); ML as compression (of data to a target function); developing a hypothesis space; developing a feature space (supervised learning); separation of training, validation and test data; the role of the task and the performance metric; the role of causal inference; the role of explanation; design choices and inherent trade-offs; bias-variance trade-off; accuracy-interpretability trade-off; speed-accuracy trade-off; accuracy of the hypothesised target function (in terms of data); correctness of the hypothesised target function (in terms of what the data refers to: ‘reality’)
Behaviourism, nudge theory and neo-platonism: machine-readable approach to human conduct; beliefs about the predictability of irrationality; assumptions about the mathematical nature of reality