Software That Reads Laws: PenaltyAI Search – Global Risk & Compliance Redefined

About a year ago I sat with my CTO in the Manhattan office of one of the world’s largest accounting firms. Their regulatory compliance global team was very impressed from what we’ve done so far with Global-Regulation and wanted to know what more we could do. As usual with large firms, they wanted a system that does everything – from tracking new bills to predicting the future (step 10 instead of step 3).
The ambition to create the ultimate risk and compliance system stuck with us. This ambition came into life when we realized, in one of our internal discussions about our global law search engine that penalties are the kind of information that can be identified with a high degree of certainty by an Artificially Intelligent system.

My story begins in the 2000s when I helped the Israeli court system work with IBM to digitize legal information. I’ve seen the slow evolution of legaltech and listened to the ambitious ideas of tech people. But I’ve also seen the reality of legal technology and wondered: how can we give machines the insight of lawyers?

Fast forward to 2017, after seemingly endless testing, experimenting, coding, consulting (thank you to Kyle Gorman from Google for the words to numbers converter recommendation) and hard work – we are extremely excited to present the PenaltyAI Search – the first and only AI system that identify compliance clauses in legislation on a global scale, extracts the actual penalties amount and serve it all to the user in US dollars.

Now risk and compliance professionals can search and identify risk levels across jurisdictions on a specific topic without even reading the law. Lets say that you are an IBM executive considering global expansion of your Watson services to new markets – with a click of a mouse you can now use the PenaltyAI Search feature of Global-Regulation to learn what would be the risk level of your goal.

Screenshot of PenaltyA Search for "tobacco nicotine"

Combine this with our complexity feature, suggested search ideas and related laws – and a risk & compliance team can feed Governance, Risk and Compliance (GRC) platforms with all the information needed to launch a new business line, in a matter of hours. Before, this would have taken months, require an army of translators and a division of analytics to determine risk and compliance.

We see this as a great achievement on several levels:

  1. an AI system that can really read legal text and produce useful meaning; and,
  2. enabling risk and compliance professionals to explore real and relevant data on a global scale, in English; and,
  3. allowing governments and businesses to assess and enhance their compliance efforts; and finally,
  4. for researchers to compare and contrast risk and compliance data globally.

Thank you big accounting firm for teaching us that even seemingly unsuccessful business meetings can bring great results. Thank you Microsoft Canada for your help in connecting us with the Microsoft Translator team. Thank you LegalX (now LawMade). Thank you Ken Thompson for UNIX and regular expressions. Thank you to my wife and children for your daily inspiration.

If you’d like to know more about how the system works technically, my CTO has written a blog post on building PenaltyAI Search.

Computers can now tell us about penalties for world laws.

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Graphing the World’s Laws: Visualization of 1.55 Million Laws + Our PenaltyAI Search

The graph above is the first time that penalties for non-compliance with the world’s laws has been visualized. It was made possible by the culmination of Global-Regulation Inc.’s R&D efforts over the last year to create an automated AI method for reading penalty provisions from civil laws – see the system here.

Our system (that we’re calling “PenaltyAI Search”) is now able to extract penalties from legislation (statutes and regulations) and present them in US dollars, along with the original text. This is a multi-phase process that starts with an AI based algorithm that identifies the penalty clauses. The next step is to extract the penalty amount from the penalty clause. This step includes complex linguistics mechanism that can convert amount in words into numbers like “one hundred thousand” to 100,000, and Indian English notation like “lakh” and “crore”. The next step is to convert different notation systems into a standardized decimal format (e.g. “560,99” to 560.99).The final step is converting all the world’s currency’s into USD to enable comparison on a global scale (which is done on an ongoing basis to account for currency fluctuations).

As for the graph at the top of this page, it was created by applying PenaltyAI Search to all of the laws in the Global-Regulation.com database (currently around 1.55 million laws from 79 countries) and then excluding countries with only a small number of laws available or too few penalties to make any useful statistical inferences. We’re making available the Excel file for the graph here: World Penalties – Feb 9 2017. We’ve excluded any penalties other than those within the top twenty most frequent for each country in order to eliminate outliers.If you make any use of this data please link back to this blog post and let us know by pinging us on Twitter @globeregulation.

The PenaltyAI Search system has been implemented into the Global-Regulation.com search engine and soon (within the next week) the user will be able to search, explore and drill down for a given topic, across jurisdictions or filtered by country. As usual, these features will be accompanied by our innovative visualization display.

We see this system as a ground breaking event in the field of extracting valuable information from legal text using algorthmic methods. On the theoretical level this is proof that the text of legislation can be mined for insights, and on the practical level, this is a celebratory milestone for compliance and GRC professionals that will be able to use our system to simplify their work.

Congratulations to our technical team that enabled us to go to where no legal tech product has gone before.

More updates will be available in the next edition of our newsletter and will be rolled out to subscribers shortly thereafter.

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Designing an AI assisted Compliance System – A Theoretical Framework

This post offers a theoretical framework for the design of AI assisted compliance system that would be able to identify compliance clauses and fines within legislation, thus enabling enhanced compliance by identifying the regulatory risk, especially to the corporation, according to the risk magnitude and its consequences for the corporation.

AI is the theory and development of computer systems able to perform tasks that normally require human intelligence. Examples include tasks such as visual perception, speech recognition, decision making under uncertainty, learning, and translation between languages.

There are two aspects of Artificial Intelligence (AI) that are relevant to the legal field: the use of AI to benefit the legal profession and the second is the implications of AI on our lives and the role of law in this respect.

Venture capital investments in companies developing and commercializing AI-related products and technology exceeded $2 billion since 2011. Leading players like IBM, Google, and Facebook have invested heavily in developing their AI capabilities.

No doubt the alleged AI legal application that has received the most public attention is ROSS, a system supported by IBM’s Watson division. One of the co-founders of the Ross team describes it as “[b]asically, what we built is a [sic] the best legal researcher available“. Even without being made available to the public nor presented in  any public demo, ROSS has become a symbol of legal AI technology.

Another new application is Global-Regulation.com, the world’s largest search engine of legislation. Global-Regulation.com makes extensive use of both Microsoft and Google’s machine translation to offer laws from China, Mexico and Spain, among many others, in English.

Other fields in which AI has been used within the legal profession are e-discovery (Recommind, Equivio – now part of Microsoft), forecasting outcomes of IP litigation (Lex Machina), providing fact and context-specific answers to legal, compliance, and policy questions (Neota Logic) and contract lifecycle software, including discovery, analysis, and due diligence (Kira Systems and KM Standards).

It should be mentioned that while many companies are claiming to use AI in their products, not all of them actually use it or have it at the core of their product. A good indication of whether AI is actually used (albeit hard to determine as the backend is not usually transparent) is whether there is training of a model embedded in the system.

On the other hand, fear of AI’s implications has grown respectively. Oxford University researchers estimate that 47 percent of total US employment is “at risk” due to automation of cognitive tasks; Silicon Valley entrepreneur Elon Musk invested in AI “to keep an eye” on it, claiming it is potentially “more dangerous than nukes;” and the renowned theoretical physicist Stephen Hawking has said that AI may create “machines whose intelligence exceeds ours by more than ours exceeds that of snails”.

In 1939 after demonstrating a nuclear chain reaction, Leo Szilard, one of the leading scientists developing the experiment wrote: “We switched everything off and went home. That night, there was very little doubt in my mind that the world was headed for grief”. Stuart Russell, a leading AI expert argues that we may be in a similar situation with AI: “To those who say, well, we may never get to human-level or superintelligent AI, I would reply: It’s like driving straight toward a cliff and saying, “Let’s hope I run out of gas soon!”. One major concern about AI is the option that advanced AI system will use their superintelligence to design and build even more advanced AI systems without any human intervention or supervision. A form of this has been taking place for years in the world of CPU development. Computer chips are so complicated that computers are required to design them. The movie Ex Machina offers an illustration of the alarming consequences that could be in store for humanity.

Stephen Hawking wrote that, in the short term, A.I.’s impact depends on who controls it; in the long term, it depends on whether it can be controlled at all. Two examples illustrating Hawking’s concern are autonomous killing machines currently being developed by more than 50 nations. Equally ethically complex are the advanced data-mining tools now in use by the U.S. National Security Agency (and their counterparts around the world).

Given these concerns, Omohundro argues that intelligent systems will need to be carefully designed to prevent them from behaving in harmful ways. He identifies a number of “drives” that will appear in sufficiently advanced AI systems of any design. Similar precautions are suggested in a paper co-authored by researchers from Google, Stanford Uni., Berkely Uni., and open AI, presenting a list of five practical research problems related to accident risk.

Since the global financial crisis of 2008, spending on compliance has break new records. Estimates report over $1 US trillion is spent worldwide on regulatory compliance, and over 1 million people employed around the world doing regulatory compliance.

KPMG, one of the four big global accounting companies claim that, “The top risk perceived by senior executives is the growing regulatory pressure from governments around the world. C-level executives in almost all industries say this, not just those in Financial Services, where companies are facing arguably the greatest regulatory challenge in their history.”

The bottom line, in the words of one senior executive we’ve spoken to is that “every $1 spent on compliance, saves $5 in fines.”

AI Assisted Compliance System

The capability of AI is yet to be utilized for the enhancement of legal compliance. This suggested system will identify fines within legislation by training a model to identify fines and charges within laws and create a compliance system that will be able to identify the greater risk to the corporation.

In the first stage, legislation from 8 countries will be considered: UK, Canada, USA, China, Indonesia, Australia, New Zealand and Uruguay. For each country legislation database an initial Search string: (corporation or company) and (compliance or fine or charge or penalty) will be employed. We’ll be using our database of laws, including the translations for Indonesia, China and Uruguay.

This manual process will identify 100 samples of fines in legislation and 100 samples where number digits in legislation are not fines (e.g., interest, budgets etc.). The next step will engage machine learning algorithm to ‘translate’ the positive and negative samples into numerical values that will be used towards automating the system.

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