A first step in developing a new hermeneutics is to develop an understanding of the meaning of relevant concepts in the domains of law and computer science.
Such concepts form part of a web of meaning, where concepts have a specific position in relation to other relevant concepts and function in the context of a domain-specific methodology.
For lawyers and computer scientists and engineer to even begin to understand each other, they will have to get acquainted with each other’s language games: with the way they USE relevant concepts when doing law or when building a computational architecture.
The purpose of this exercise, therefor, is not to develop a shared vocabulary, as this would imply mixing and confusing methodologies and - in a sense - betraying one’s own methodological integrity.
The purpose is to take a first step in the process of learning a new language, at least to the point of understanding where specific concepts fit, how they affect the outcome of both law and computational design and - in the end - how the inherent logic of both fields of application impact the kind of protection that law aims to offer. The second step, therefore, concerns the grammar of both fields, emphasising the dynamic and generative structures that drive the practice of law and the practice of computer science.
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.
Vocabulary of law
Legal effect, legal subjectivity, legal right, legal obligation, legal duty, liability, legal norms, legal certainty, justice, equality, reasonableness, sources of law, legislation, regulation, case law, doctrine, fundamental legal principles, customary law
Vocabulary of computer science
Information, data, code, cryptography, encryption, hashing, blockchain, distributed ledger technologies, verifiability, attack model, key management, security, confidentiality, integrity, availability, machine learning, training dataset, validation dataset, test dataset, feature space, hypothesis space, target function, performance metric, objective function, accuracy, precision, sensitivity (recall), optimisation, approximation, baseline, ground truth, null hypothesis, Godel’s incompleteness theorems, Church-Turing undecidibility theorem