Search Ideas – Interaction with the Search Engine

What if you could discuss your search query with the search engine? well, now you can. Our new feature suggest search ideas based on the user’s query. These search ideas are extracted from our world’s laws database itself.

Here’s how it works:
1. We take the text of every law in the world and extract the most frequently mentioned word pairs, on a per-law basis. This way we create a new database of word pairs.
2. When someone does a search we check the database of word pairs and take the word pairs that occur most frequently in association with the word or word pair that the user is searching for. So a search for “coffee” will return keyword suggestions for words that appear in laws that mention “coffee” most commonly.
3. We then filter the words and take the best matches and display those to the user. These are the search ideas.

You can click on the search ideas in yellow at the top and it will be updated according to your recent search. For example, lets say you started with Coffee –> then you choose ‘Coffee Agreement’

And then choose ‘system certificates’. This is endless.

This new feature actually enable you to interact with the search engine and follow the trail that is based on the database of word pairs we created from our gigantic database of the world’s laws.

 

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LexisNexis vs. Westlaw: How Many Countries Can You Search?

Which countries can be searched on global legal research platforms? According to our research, Westlaw (as of 2017) has legislation search for 14 countries (counting the EU as a country) and LexisNexis has 12 countries.

Westlaw (Thomson Reuters) and LexisNexis (RELX Group) are the two largest legal research companies in the world. Wolters Kluwer is a close third place, and in some jurisdictions is the main legal research company (they’re about 50/50 EU and North America). All of these companies offer a bewildering list of databases and sources, and none of them bundle multiple country search into one search engine. On one webpage, Westlaw claims that they make available 28,000 different databases worldwide.

According to our research, these are the countries for which LexisNexis has primary legal research search (i.e. national laws in a searchable format):

US, Canada, Australia, New Zealand, France, Ireland, India, UK, EU, South Africa, Hong Kong, Malaysia and Japan.

Westlaw (Thomson Reuters) offers the following countries:

Australia, New Zealand, Canada, UK, EU, USA, Philippines, Qatar, Iraq, UAE, Hong Kong, Argentina, Paraguay and Uruguay.

LexisNexis Sources: http://www.lexisnexis.ca/en/support/resources/KM_LNQLFullServiceInt.pdf, https://www.lexisnexis.com/fr/droit/, http://www.lexisnexis.co.za/, http://www.lexisnexis.com.hk/en-hk/product-line/legal.page, http://www.lexisnexis.jp/ja-jp/Products/lexis-asone.page, http://www.lexisnexis.co.in/en-in/products/lexis-india.page, http://www.lexisnexis.co.nz/en-nz/home.page. One Lexis page, http://www.lexisnexis.com/en-us/products/global-law-news-business-resources.page, notes that there are nine countries in total but that doesn’t seem to be the case.

Westlaw Sources: http://westlawinternational.com/our-solutions/, http://www.westlaw.ie/, http://westlawgulf.com/inside-west-gulf/legislation/, http://www.laleyonline.com.ar/, http://www.laleyonline.com.py/, http://www.laleyonline.com.uy/, http://www.thomsonreuters.co.nz/westlawnz, http://www.westlawasia.com/philippines.

A caveat to the above infographic: there may be countries that either one of these companies offers legislation search for that they either don’t advertise or is very difficult to discover. They essentially operate as independent businesses in many countries and have other subscription services that are sub-licensed, so there may be other flags missing from the infographic above. If you discover a missing country please let us know so we can update this blog post.

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Teaching with Global-Regulation

screen-shot-2016-12-03-at-8-33-00-pmI had a wonderful experience with my 4th year Law & Technology students last Thursday. I asked them to search Global-Regulation.com for privacy laws that relates to teenagers and then create a scenario that describes these rights in a way that teenagers can understand.
After creating the scenario, the students, working in groups, needed to choose the pictures for each square of the scenario and we uploaded it to a website I created for this purpose – Privacygames.com.

The results were amazing and the students were fascinated both by the legislation search in Global-
Regulation, and with creating the scenarios.
The best scenario was an illustration of legislation that is set to protect the privacy of teenagers by determining that a physician has a discretion to report a pregnancy of a girl under 16 to her parents if he feels that she is not capable of dealing with the sitscreen-shot-2016-12-03-at-8-32-22-pmuation.

Another scenario was describing new legislation in New Zealand that makes it an offence to engage in ‘revenge porn’.

Empowering teenager’s by informing them of their rights and obligations is an exciting field that should be fostered. Using Global-Regulation
for class exercise is really intriguing for the students.

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The First Law Chatbot: GRBOT

Screenshot of chatbot on iOS
We are very excited to announce the launch of the first ever legislation chatbot: GRBOT, using the Kik mobile platform. The GRBOT enables the users to type a request: e.g., “search for United States drone laws” or “Show me EU laws about organic farming”, and the GRBOT will send the most relevant laws to the users’ mobile device, with a link to see more.

You can see the bot in the Kik Bot Shop here: https://bots.kik.com/#/grbot.

The only thing needed to connect to the GRBOT is to download the Kik app from the app store and friend the GRBOT account. When you first start chatting a message is displayed that shows a few examples of how to interact with the bot.

We think we’re the first ever legislation chatbot but there have been other legal bots created, both on chat platforms and accessible through other services.

Other legal bots already around are a lawbot.info – built by Cambridge students to advise sexual assault victims and lawbot.co, designed to analyze contracts. Others include DoNotPay, created by an 18-year old British coder, that quickly handles ticket appeals through a Q&A chat; and of course ROSS (but see skeptical voices). See also Lexi – “You can chat to Lexi to generate a free Privacy Policy or Non-Disclosure Agreement”. See also Fastcase bad law bot.

Screenshot of Kik Bot Shop

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Machine Learning – Text Analytics Comparison

As part of our work engaging Artificial Intelligence and especially Machine learning into Global-Regulation‘s system, we’ve conducted a comparison between the big four providers of ML Text Analytics: Microsoft, Google, IBM and Amazon. This post is a follow up of a previous post regarding AI assisted compliance system.

MicrosoftMS ML studio allow some options of text analytics.

screen-shot-2016-11-11-at-9-50-29-am

Although not particularly helpful for the purpose of identifying segments within legislation, MS ML studio

dn781358-mccaffreymls_fig1_hiresja-jpmsdn-10 is the most friendly system among the ML tools in this comparison. It is so friendly that even a user with minimal background in programming and ML can use it (with some patience and strong will 🙂

In MS ML There is a link to new text analytics models but unfortunately it is a broken link.

GoogleTensorflow offers some text analytics features. This is not a friendly tool and the text analytics options it does offer are vague. However, the vector representation of words may be useful when analyzing legal text and training a model to identify segments within legislation. This is a different approach than the structured text analytics offered by MS and IBM – see below.

screen-shot-2016-11-11-at-10-08-39-am

In the context of a previous post about AI assisted compliance system, Tensorflow vector representation may be the solution for the first part of the challenge, i.e., manually identifying compliance clauses and training the model with these clauses. Nonetheless, new challenges arises in the implementation stage since the system will be able to identify laws that includes compliance clauses but not the specific clauses within the law.

Overcoming this challenge will require an additional stage in which the laws may be broken into chunks of text before running the model to identify the clauses. As laws are not always (and usually not) machine friendly, this process creates its own challenges.

IBM – Now offered through AlchemyLanguage, IBM now have one text analytics feature analyzing entities and relevance. Before migrating the text analytics features in July 2016, IBM offered few options of text analytics that are not available now.screen-shot-2016-11-11-at-10-20-11-am

This system analyze factor as ‘Fear’, ‘Anger’ and ‘Joy’ – not exactly what one would need to analyze legal text. In addition, IBM’s costumer service does not really work. Attempts to get access to their system failed even after stubborn emails.

Finally, it should be mentioned that Amazon’s ML platform  does not provide any text analytics options.

Conclusion

One would expect that the first step in analyzing legal text would be to use ML text analytics options. This seems like the short way towards identifying segments within legislation and the best way to ride the advancements in this field. However, upon testing these ML text analytics abilities, it becomes clear that this is not the answer and that in their present state of development, ML text analytics is not capable of doing much serious work, rather than classifying text as ‘Joy’ or ‘Anger’.

The more ‘simplified’ approach taken by Tensorflow vector representation is much more relevant for the purpose of analyzing legal text and identifying segments in big data even though it is far from the ‘Watson Dream’ where you ‘work with Watson’ and get your text analyzed with the click of the mouse.

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Finding Foreign Laws in English

You can read translations of over 750,000 foreign laws using Global-Regulation.com. Just search for the the phrase and click a law from a non-English jurisdiction. A machine translation of the law will be shown on screen and you can click through to see the law in the original language.

ISO codes on the database coverage page

If you go to our coverage page you’ll see a list of our data sources. The bracketed codes to the right of the region title are the ISO codes for the language. The screenshot to the left shows a few examples of this. Note that “zh” is Mandarin, “es” is Spanish and “cs” is Czech.

As of mid-October, 2016 we have over 25 languages translated to English.

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Upgrade to MySQL Cluster

We upgraded our MySQL-based law database system a couple weeks ago. We’re now running a cluster with a writer and reader so that the failure of one server doesn’t result in downtime for our users. This new cluster-based system is approximately 15x faster than the old one due to a much higher amount of RAM (necessary to accommodate our rapidly growing index of laws and translations). Although we’ve updated many other parts of the system, we had been running on the same HDD-based MySQL system since October of last year. Our new system is SSD-based and has far higher throughput.

Users will primarily see the difference in how much quicker the “Related Laws” feature is. We’ve measured the performance at about 15x the previous MySQL server system. Thanks to our cloud-based infrastructure and standardized components it was quite easy to make the transition. Within about two days we went from experimenting to a full transition (and no reported downtime from users).

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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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Co-Founder’s Blog Post About Global-Regulation.com

One of our two co-founders has written on his personal blog about the journey from idea to world-leading law search engine that Global-Regulation Inc. has taken. You can read the blog post here: https://www.cameronhuff.com/blog/idea-to-world-law-search-engine-in-one-year/index.html.

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Related Laws Feature

We’re pleased to announce that our “Related Laws” feature is now generally available (and fast). When users click the “Related Laws & More Info” button next to each search result there will automatically be a list of related laws generated and shown (where applicable – short laws don’t have this feature because the results aren’t useful).

This feature was previously available and marked as “experimental”. With recent upgrades to our database system we’ve improved the speed of this feature by 15x and can make it widely available to everyone (including users who are not paid subscribers). This is one part of our strategy for making the search experience faster and more useful.

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