MIT’s theory on machine readable regulations is already a reality at Aptus.AI


What links Pisa to Massachusetts? A new machine readable regulatory format

The accessibility of information really matters to us, as it represents the basis to give everyone the possibility to understand the world around us. At Aptus.AI this vision took the form of a new machine readable regulatory format, which allows people to interactively access legal documents in real-time. And we are very happy to discover that the Massachusetts Institute of Technology is on the same page as us. We published our first post on machine readable regulations more than one year ago, some months before MIT's study titled “Drafting X2RL: A Semantic Regulatory Machine-Readable Format”. Actually we discovered this paper with some delay, anyway after our further articles on this issue. The first was about some philosophical aspects, namely that regulations, for their nature, cannot be “fully machine-consumable in the sense of being able to be executed directly by a machine”, in the words of MIT researchers P. A. McLaughlin and W. Stover. While the second was focused on financial regulations, following a report by the European Banking Authority about RegTech. And currently the topic is increasingly central in the regulatory digitalization discussion.

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A parallel path from Aptus.AI to MIT under the sign of a semantic machine readable format

Obviously we are proud to share the same MIT path in respect to a new regulatory machine readable format. Especially in respect to the possibility of implementing a semantic type of that, as an evolution of the already existing syntactic formats. Trying to make it simpler, we can say that MIT researchers use this latter term to define both the USLM XML format for U.S. regulations and the UN Organization for the Advancement of Structured Information Standards (OASIS) international schema, called Akoma Ntoso (which means “linked hearts” in the Akan language of West Africa). These two standards “emphasize improving legibility of the internal structure of legal documents, rather than their content [...] and focus only on legislative management costs”, while McLaughlin and Stover propose a new semantic format “that could also help reduce the economic costs of complying with regulatory and other legal restrictions”. Well, at Aptus.AI we have been working in the same direction, namely on a machine readable format that takes into account “also documents content, meaning, and external structure [...] to help reduce both the management and economic costs of legislation and regulation”. Yes, we’re still quoting the words by MIT researchers, as they express very well what our AI proprietary engines do with regulations. Our technologies exploit Artificial Intelligence to make them machine readable, therefore digitally accessible. Through a semantic format that “can also make the content and intent of legislation and regulation more legible to machines”, thus enhancing regulatory documents.

An overview of X2LR standard: similar approaches in different contexts

To sum up, the Massachusetts Institute of Technology study proposes a new machine readable format called X2LR (eXtensible Regulatory Reporting Language), which can be defined as a standard representation of what our proprietary technologies put into practice. “X2RL is a new semantic machine-readable format that reduces these costs by enhancing regulatory documents and other formal and informal legal documents with rich metadata fields that inform machine and human readers of the types of effects a document will have [...] and how restrictive a particular document is”.  And again: “semantic format enables machines to programmatically walk through all levels of the body of legislative and regulatory text in a structured manner that more clearly relates documents to each other. This could improve communication between machines and these documents, and consequently make it easier for people - including the legislators and regulators themselves - to build better tools for understanding these texts”. And that’s exactly what we’ve achieved with Daitomic: building a tool to help professionals access and understand financial compliance regulations. Obviously the USA regulatory context is different from the European one, but the same principles expressed within the MIT study can be successfully applied to the documents enacted by the EU, the EC and - more in general - by all of the existing national and international authorities through their legal sources. “These tools will help improve the accessibility of legislation and regulation, assisting with comprehension and compliance with legal restrictions” to “reduce the economic friction of legislation and regulation, and also enable new tools that benefit legislators and regulators”. Once again, what the MIT study is saying can be used without significant change to express the mission of our AI financial compliance management platform, Daitomic. Or, more generally, what we aim to achieve with our Artificial Intelligence technologies, created to generate a new machine readable regulatory format.

An effective machine readable regulatory format at work: Daitomic success case

To properly close this synopsis about the Massachusetts Institute of Technology study “Drafting X2RL: A Semantic Regulatory Machine-Readable Format, it is necessary to present some possible use cases of this standard. Starting from the ones enlisted by the authors, it is not difficult to relate them with the already existing features of Daitomic - which integrates our AI technologies with the Akoma Ntoso legal standard, thus making banking regulations machine readable, which allows for different regulatory texts enhancements:

  • finding relevant documents through an advanced semantic search and the related filters by topic or type;
  • reading an individual document, namely having the possibility to highlight different parts of the text, in relation to their nature, and to tag them in respect of the reader’s interest;
  • aggregating relevant documents for analysis, that is to put together all the laws or law parts relevant for the ongoing activity, in order to prepare a gap analysis.

The only relevant distinction between Daitomic’s machine readable regulations and the X2LR format proposed by the MIT is the need of this latter to “clarify localized definitions and requirements”, as this element is more related to the different uses of the same legal terms in different states or the geographical areas within the USA, while both the Italian and the European regulatory systems are more standardized in respect to the use of legal expressions. Well, to conclude this analysis, we can say that the proposal by MIT researchers of a new semantic regulatory machine readable format has been realized several miles away from Massachusetts. More precisely in Pisa, thanks to the work of the Aptus.AI team, that created Daitomic. Surely Italy is quite far from the USA, but great minds are linked by big ideas and high goals. And we’re proud to share them with a worldwide recognized institution like the Massachusetts Institute of Technology. If you are curious to pass from words to facts, just click below and book a demo to see how our machine readable format is exploited within Daitomic!