The purpose of legal texts, and more specifically of authoritative legal texts, is to create, modify or terminate the right and obligations of individuals, institutions or other types of organisations. Examples of this genre of texts are laws, constitutions, contracts, statuses, deeds and wills.
These texts are usually written in legal language, often referred to as “legalese”. Legal texts are very different from ordinary speech, and to most people their content and structure are impossible to understand.
And still legislators and policymakers expect that their laws:
Put in “legalese” plain English: this almost seems to be a “contradictio in terminis”.
Luckily there are sound alternatives to the kind of legal writing described above to ensure that law and regulation making, testing, implementing and enforcing can finally become an efficient and effective process. One of these alternatives could be the use of Decision Modeling. Over the last few years, Decision Modeling has helped business and governmental organisations to document, test and automate business rules without the involvement of their IT departments.
Instead of writing rules in the form of unstructured text that is open for misinterpretation and inconsistency, Decision Modeling offers a methodological yet intuitive approach to break down regulations and laws into indisputable and clear facts that are linked together in logical statements. These logical statements about the application of the regulations and laws can easily be understood by non-technical and non-legal people. And their biggest advantage is that they are structured enough to be automated on the fly without needing technical IT skills.
In order to illustrate how the process of Decision Modeling works, let’s take the example of the allowance of unemployment benefits.
In most regulations, one of the specifications is either who is eligible to receive a certain benefit or who is obliged to comply with the regulation.
For the allowance of unemployment benefits the regulation will need to state who is eligible for these benefits. The indisputable facts or conditions to decide that might be the applicant’s age, the applicant’s years of employment, the applicant’s nationality, the applicant’s residence, and so on. A combination of all of these elements enables to unambiguously determine whether the applicant complies with all of the conditions to receive an unemployment benefit.
These indisputable facts are then summarised in the headings of a decision table, in which each row contains an unambiguous statement about the eligibility to receive an unemployment benefit.
Another decision table could then for the eligible applicants determine the amount of the unemployment benefit based on other undisputable facts such as last salary, allowance percentage, and so on.
Decision Modeling can first of all contribute to a higher degree of transparency of regulations and laws for all stakeholders: legislators, governmental regulatory bodies, and citizens or organisations eligible to receive benefits or obliged to comply with a regulation.
Secondly, Decision Modeling easily allows to proactively validate and test regulations, in order to avoid inconsistencies or undesired outcomes.
And finally, Decision Modeling allows to automate regulations without having to set up complex and lengthy IT projects. This would even allow legislators to provide regulation stakeholders with a centralised internet service that lets organisations such as companies, government bodies or other groupings of citizens correctly and adequately implement a regulation, which would minimize the need for setting up costly and difficult regulation enforcement forces.