Legal Tech Start-ups: State of the Art and Trends

Sabrina PRADUROUX a,1
, Valeria de Paiva b
and Luigi di Caro b
aUniversity of Turin, Italy
bNuance Communications, Sunnyvale and University of Turin, Italy
Abstract. We survey the legal tech market, classifying and analyzing a number of
legal start-ups, particularly the ones in Silicon Valley, where the first author was
based for her graduate summer project with the second author. This kind of survey
of the state of the art is inherently incomplete, very tied-up to where and when it
is done, and frankly biased towards the interests of the authors. However, if read
paying attention to these caveats, this survey can be very useful to practitioners,
interested in uncovering the landscapes of the market.
Keywords. legal start-ups, state-of-the-art, legal tech, legal informatics market
1. Introduction
Legal informatics (legal information technology and its study) is concerned with the
social implications of informatics use, as well as with all the applications of informatics
in the field of law, such as the storage and the automatic retrieval of sources of law,
the automation in law offices and in the judicial administration and all the other uses
of the computers in law (data bases; information systems; educational programs; expert
systems, computer-aided legal drafting, etc).
Research and development of computational law the branch of legal informatics
concerned with the automation and mechanization of legal analysis is growing at fast
speed. Many of the new tools and methods come from research in academic institutions,
some comes from development done by the big law practices, but most, or so it seems
to us, comes from legal start-ups. LEgal start-ups are small companies with one or two
founders, a few dedicated hackers and sometimes some venture capital. The statistics
are somewhat difficult to verify, but the Stanford CodeX group (http://tech.law.
stanford.edu/) mentions at the time of writing (October 2016) 576 companies in the
space of legal innovation tech companies. Clearly we will not be able to survey all of
those. But we aim to indicate in the next section, in broad brushstrokes the main features
of this landscape of legal companies.
1Corresponding Author: Sabrina Praduroux, University of Turin, Italy, E-mail:sabrina.praduroux@unito.it.
2. A Landscape of Legal Start-ups
Broadly speaking we are interested in describing what has been called the Legal Tech
market. Legal Tech, like its older and more substantial ‘brother’ FinTech (Financial Technology)
covers companies (mostly startups) utilising technology to build products solving
problems faced both by the legal industry (i.e. law firms, corporates etc.) and consumers
of legal services.
Some blogs and analysts2 have claimed that, while financing activity to startups
targeting financial services has spiked over the past five years, startups in the area of
legal services have seen no such boom. Since 2011, global legal tech companies have
raised just $739M in aggregate funding, according to the same source. This is despite the
ostensible opportunities in the multi-billion dollar legal industry. Of course to measure
growth of an industry one needs to define what constitutes Legal Tech and boundaries
differ depending on the commentator.
In this work, we are not worried about economic growth of the market, quarter by
quarter, but instead we are concerned with detecting the most useful and most feasible
technologies, aligned with our own academic profiles. The writers have profiles associated
with work in Artificial Intelligence (AI), logic and ontologies, as well as Natural
Language Processing (NLP) and tools. Thus we spent a long time considering the kinds
of classification of companies in Legal Tech that we should help us in our usual academic
concerns.
Whether the growth is explosive or not, whether it could or should be faster or not,
in any case, we are seeing increasing numbers of well funded legal startups. This in turn
raises the profile of the industry and provides further validation for startups looking to
get those first customers in a notoriously risk-averse market. The legal services market
has historically been viewed as one that is hard to penetrate, when it comes to knowledge
acquisition, as liability, therefore costs, can scalate rapidly. The introduction of marketplace
models and startups focused on document services is improving this situation. The
increased transparency is presenting opportunities for startups to compete with the more
well-established players.
Advances in natural language processing have enabled people to build solutions
addressing various domains within the overall legal tech market, and we discuss these
verticals in the next subsection. Language solutions are taking advantage of large data
sets to assist with the automation of certain low level repetitive tasks. The opportunities
here to reduce costs are significant as law firms tend to bill by the hour. As this technology
improves and the data sets they work on are scaled up it could be possible for solutions
to be built to automate more advanced work [3].
We are seeing the emergence of startups that are aiming to go beyond the automation
of documents and of repetitive tasks and which aim to provide additional insights
into Legal Research. There is an increasing interest in the space from both those
within the law and those building solutions from outside the law, aimed at the sector.
As a representative example, that has been vastly discussed in the news we can
discuss the start-up “DoNotPay”, created by Joshua Browder. Wikipedia describes the
launching of this legal start-up thus ”On 12 January 2015 it was announced that Browder
created the UK’s first ’robot lawyer’. He ultimately hopes to replace ”25,000 ex-
2See https://www.cbinsights.com/blog/legal-tech-startup-funding-2016.
ploitative lawyers” with robots which can respond to questions with human emotions
powered by artificial intelligence.” The fact that this company has saved British motorists
in excess of $4 million (according to Fortune magazine in May 2016, http:
//fortune.com/2016/05/21/bots-rise-up/) shows that this kind of company is
worth paying attention to.
We considered a couple of classifications of the kinds of companies in Legal
Tech. One classification we looked at was the work of Janine Sickmeyer (https:
//lawyerist.com) which has 12 categories. Some of these could be merged and then
they looked more like the classification, coming from the website of CodeX, the Stanford
Law School Center for Legal Informatics3
. When discussing companies, in their
database of legal start-ups, the Center came up with a classification4
that has 8 types of
legal companies: Marketplace, Document Automation, Practice Management, Legal Research,
Legal Education, Online Dispute Resolution, E-Discovery, and Analytics. Some
thinking about these labels and the kinds of tools we are most interested in, gave us the
eight categories that we present below, which seem to us not too specific, but not too
generic either.
3. Types of Legal Tech
We now describe the eight categories of legal tech that we decided to concentrate on. The
first three categories are similar to what has happened in other areas of human activity,
where computers have been incorporated into the existing workflows to help with data
ingesting and management, using spreadsheets, databases and email and online forums
to grow business. For each of the categories we give some labels we have seen associated
with the category.
1. Lawyer Marketplace– Lawyer-to-Lawyer Outsourcing – Social and Referral Networks.
These are online marketplaces connecting lawyers with clients, either end
users or other lawyers.
2. Document Automation and Assembly – DIY Legal Forms and Contracts This
category encompasses the design of systems and workflows that assist in the creation
of electronic documents. These include logic-based systems that use segments
of pre-existing text and/or data to assemble a new document.
3. Practice Management – Case Management for Specific Practice Areas – Legal
billing. Practice and case management software provides attorneys with convenient
methods for effectively managing client and case information, including
contacts, calendar and meeting information, documents, and other specifics. All
that is involved in facilitating automation in law practices can be considered practice/case
management software.
The main features and functions of case management packages are:
• Case Management Information on cases and matters can be made accessible
through a centralized database. This database can manage to-do lists; can provide
fast and flexible searching; can check conflicts of interest; and can check
statues of limitations, for example.
3https://law.stanford.edu/codex-the-stanford-center-for-legal-informatics
4http://tech.law.stanford.edu/
• Time Tracking Records are systems of billable time on an hourly, contingent,
transactional, or user defined fee, computed individually or firm-wide. Links
to time, billing, and accounting programs are essential parts of these systems.
They can also generate client invoices; link to other time tracking and accounting
programs; and create reports for individual billing attorneys.
• Document Assembly and Drafts documents. These require links to word processing
programs and templates to facilitate creation of most common documents.
• Contact Management tracking systems log, and store details about phone calls,
e-mails, and other correspondence. They can also provide callback reminders
and deadline tracking for processes.
• Calendaring and docketing software allows staff to view tasks, deadlines, appointments,
and meetings by day, week, month, or year. They can calculate
calendar dates and schedule appointments and meetings.
4. Legal Research. Legal search engines based on advanced search technology from
the fields of artificial intelligence, data mining, and natural language processing,
with different characteristics and features are available.
5. Predictive Analytics and Litigation Data Mining. Predictive analytics is the analysis
of data through statistical or mathematical techniques that results in meaningful
relationships being identified in the data. These results can then be used
for better prediction of future events and better decision-making. Predictive modeling
of litigation management provides the information needed at the beginning
of a juridical process to improve it.
6. Electronic discovery (also called e-discovery, ediscovery, eDiscovery, or eDiscovery).
This is the electronic aspect of identifying, collecting and producing
electronically stored information (ESI) in response to a request for production in
a law suit or investigation. ESI includes, but is not limited to, emails, documents,
presentations, databases, voicemail, audio and video files, social media, and web
sites. This is one hard problem as the law mandates that all legal evidence need
to be uncovered in law suits and the enormity of the task is staggering.
7. Online dispute resolution (ODR). This uses technology, especially the Internet
to solve disputes out-of-court through an Alternative Dispute Resolution procedure.
There are two basic branches of ODR, both based on different kinds of
technology. The first branch may be called Technology based, it refers to those
systems where technology plays an active role in conducting the dispute resolution.
A prominent example of technology-based ODR systems are blind-bidding
systems. The technology uses multivariate algorithms to help parties arrive at
the optimal outcome. The second branch of ODR consists of technology-assisted
solutions. Technology-assisted ODR refers to the use of technology to augment
Alternative Dispute Resolution processes that exist independently of the technology.
8. Data security technologies. These are intended to protect confidentiality of data
that is exchanged in client/server data transfers. Fundamental to these technologies
is the use of proven, industry-standard encryption algorithms for data protection.
Our classification is not without problems: for example, we have decided to give
e-discovery its own class, when we could have considered it part of practice manage-
ment (as it is part of the workflow of practices) or litigation data mining/analytics or even
the more generic legal research. All of litigation data mining could also be considered
a subset of legal research. But our classification is pragmatic and since most of innovation
ocurring in legal research seems to occurr in these two categories (e-discovery and
litigation data mining), it seems reasonable to give them each its own top category.
Also our last top category can be considered not strictly ‘legal’ technology, as computer
security experts would claim it as their territory. While many lawyers would prefer
not to deal the mechanics of ensuring privacy and confidentiality of transactions, this
seems a field where engineering by itself is not enough and the the legal profession must
interact with computer experts to ensure that the tools developed satisfy their needs. Similarly,
there is plenty of work on the borders between Medical Informatics, the Law and
AI that need to be addressed. Given that biology is providing data that is far beyond the
ability of humans to fully analyze and that the ability to integrate data about patients
from streams genomics, personal monitoring and electronic health records to accurately
diagnose and choose treatments, it is imperative to think about the anticipated evolution
of these capabilities and how they might impact economic, social, political and cultural
activities. But the scope of our research has to be made feasible, so we will not dedicate
any attention to either Medical Informatics or Data security technologies or even online
dispute resolution, because the tools that we have at our disposal are not the most suitable
for this kinds of technology.
Another area that is really important nowadays, and one where our tools (Natural
Languague processing, knowledge bases, logics and ontologies) can help, but we will not
try to address in this report is Intellectual Property laws and management. Patents and
their multiple issues require specific subject matter expertise that we could not master
in the short time we dedicated to the task of surveying the landscape of legal startups.
Similarly we will not discuss the area of Legal Education: it requires too much expertise
in Law, and it is borderline to our concerns.
4. Industrial Trends
Altogether e-discovery (# 7) and data management in general (including litigation
data mining (# 5), as well as other forms of legal research) seem to be two of
the fastest-growing segments of the legal technology market. Data from a report
on eDiscovery (Software and Services) Market Trends by Global Industry Analists,
available at www.strategyr.com/MarketResearch/eDiscovery\_Software\
_and\_Service\_Market\_Trends.asp informs us that
The global market for e-discovery (Software and Services) is projected to reach
US$11.6 billion by 2020, driven by growing demand from governments and private
enterprises, rise in criminal prosecutions and civil litigations, and increased investigational
admissibility of digital data.
While the growing trend is easy to see, the numbers on these kinds of predictions are
much harder to come by. There are too many so-called specialists online and many do
not explain their sources. Nevertheless it is clear that the digital data explosion is creating
challenges for all information technology and legal departments in enterprises all over
the globe, who need to efficiently handle and manage data for use in active litigation
and/or in internal investigations.
5. Academic Legal Informatics
Law schools, like large law firms, do not leap into new technological directions easily.
But there are some exciting things happening in some of the law schools. Stanford
(CodeX), Georgetown (Iron Tech Lawyer), Suffolk (Institute on Law Practice
Technology and Innovation) and Chicago-Kent (Center for Access to Justice and
Technology) have programs that bring new legal tech into the classroom http://
legal-tech-blog.de/2289-2.
Using the same categories that we discussed for cataloguing start-up companies
we can try to classify the academic projects in Legal Informatics. Clearly schools
and the NGOs have the possibility and some even may say the duty of using technology
to promote access to justice. This is one of the topical issues of the American
Bar Association (ABA) report [2]), and also of the UN Millenium Development
Goals (Goal 16 of the Sustainable Development Goals is dedicated to the promotion
of peaceful and inclusive societies for sustainable development, the provision
of access to justice for all, and building effective, accountable institutions at all levels.
http://www.un.org/sustainabledevelopment/sustainable-development-goals/). There
are many ways to use technology to further access to justice, as described in [1]. The
State should invest money for these kind of research projects, and it might be a good
sector for private investments as well.
Document Automation and Assembly
Stanford Computable Contracts Initiative (SCCI) (CODEX, Stanford) works on developing
a universal Contract Definition Language that will allow terms and conditions to
be represented in machine-understandable way. As a result, computers might be able to
eventually process and reason over the contracts automatically with a guaranteed degree
of accuracy. Fair Document (another CODEX, Stanford project) seeks to drive down the
cost of legal services by making the process of performing high-volume transactional
legal work more efficient. Fair Document wants to do this by automating the generation
of a base set of documents and providing collaboration and workflow tools to perfect the
documents. Since automated process are always subject to risk, before a client sees any
legal output from Fair Document, it is reviewed by a licensed attorney. Other products
already exist in the market to drive efficiency through document automation, but they do
not involve lawyers.
The project A2J Author (Center for Access to Justice and Technology – IIT ChicagoKent
College of Law) consists of a software tool that delivers greater access to justice
for self-represented litigants by enabling non-technical authors from the courts, clerk’s
offices, legal services programs, and website editors to rapidly build and implement customer
friendly web-based interfaces for document assembly.
Litigation Data Mining
The Smart Prosecution Project (CODEX, Stanford) seeks to combine the latest advancements
in data mining and data analytics to apply them to the criminal justice system.
The Computational Linguistics and Effective Legal Drafting (CODEX, Stanford)
project focuses on using advances in computational linguistics technology to help
lawyers draft more precise and error-free legal documents like contracts, regulations and
statutes. The technology underlying this project includes various natural language processing,
machine learning, and data mining techniques. Any public or private sector entity
that handles legal documents and is concerned about litigation risk would be a potential
user of this solution.
Lawyer Marketplace
Legal.io (CODEX): provides a marketplace infrastructure to legal service enterprises,
so that they can coordinate talent, services and transactions. Innovative law firms, bar
associations, and legal aid organizations, use Legal.ios collaborative, white-label software
platform to reduce overhead, while increasing revenues and engagement with new
clients.
The Legal.io technology framework was developed out of research, prototyping and
testing on LawGives, the first consumer-facing marketplace platform powered by Legal.io.
Legal.io combines AI-based algorithms and the design methods into a readyto-deploy
content-, service-, and panel-management solution,that hopes to be customtailored
to any law firm or association of legal professionals, large or small.
Legal Research
Not surprisingly many of the academic projects concentrate on Legal Research, where
the long term payoff might be more important than immediate profit making capabilities.
The Computational Law project (CODEX, Stanford) aims at enabling higher degree of
automation to achieve better usability and more efficiency of various tasks involving
legal reasoning. It focuses on formalization of governmental regulations and enterprise
policies, development of automated reasoning procedures for compliance checking, legal
planning and regulatory analysis, and developing user-facing computer systems
Wellsettled (CODEX, Stanford) is a searchable database in several areas of law,
such as patents, criminal law, and torts. The project Ravel (CODEX, Stanford) focuses
on visualization-based search of legal data. Specifically, Ravel is a search platform for
lawyers and law students with a clean, collaborative interface that provides visualization
of how legal data (primarily cases) rank and connect to each other. In addition, the platform
offers features that enable annotation of case law and collaboration across caseboards.
Ravel leverages advances in the fields of network analysis and data visualization
to show not just a cases relevance but also how case law evolves and how legal topics
cluster.
The LexCraft (Legal Information Institute, Cornell Law School) project aims to
record, refine, and promulgate best practices for electronic legal information publication.
The OAI4Courts (Legal Information Institute, Cornell Law School) is a project that
promotes the federation of independent legal websites into large, useful ”virtual collections”
that span boundaries. As a technical project, that is a goal that is within easy reach.
Well-understood standards for metadata interoperability, particularly the OAI-PMH standard
for metadata harvesting, have been widely used for similar purposes in the digitallibrary
world for several years. They are only slowly making their way into the realm of
legal information, though. One of the goals of this project is to accelerate that process.
At the same time – like any standard – success will depend crucially on buy-in from legal
information creators and from those who publish their work.
Autonomous Intelligent Cyber Entity (AiCE) (CODEX, Stanford). This project explores
the commercial and legal aspects and implications of an intelligent cyberagent
and its evolution into an autonomous intelligent cyber entity (AiCE, pronounced ice). It
evaluates and builds functional and operational schemas for standardizing AiCE with an
emphasis on reducing waste of judicial resources, increasing e-commerce transactional
certainty and expanding into new frontiers for e-commerce interactivity on business to
business (B2B), business to customers (B2C) and customers to customers (C2C) levels.
Designing and Understanding Forensic Bayesian Networks (CODEX, Stanford)
with Arguments and Scenarios. Evidence based on statistics can easily lead to errors.
This project aims to help prevent this sort of error from occurring. The projects approach
is to link the successful statistical modelling technique of Bayesian networks to models
that effectively dovetail legal argumentation and scenario construction in the legal world.
Computational law research and development (MIT). Their task is to reformat legislative
data, refactor legal code and republish the law as a public digital service. Deeper
economic, jurisprudential and socio-political dimensions of the Algorithmic Law project
will need to be addressed over longer arcs of time primarily by other stakeholders. In
any event, the substantive content of the law can not be significantly adapted for the future
until open, structured data standards are adopted as the common containers of law.
They provide a roadmap of the current approach of their project. In Phase 1 they aim
to establish workable foundations for Computable Law. The idea is to examine existing
examples of law as computable data, including thorough legal markup and thorough incorporation
by reference of technical standards into law. Then they will extrapolate scenarios
demonstrating public law as algorithms that can be defined, debated and decided
as part of open, public and democratic processes. Thirdly they plan to define one or more
prototypes, test and evaluate specific computational law use cases in relevant business,
legal and socio-technical contexts. Following that, in Phase 2 the idea is to iteratively
prototype Public Law and Regulation as computational data and algorithmic expressions.
Finally in Phase 3 the plan is to postulate and propose dynamic, emergent laws, legal
entities, legal instruments, legal relationships, legal transactions and legal systems, in
general. It all seems very ambitious and not really developed enough, yet.
6. Comparing Landscapes
One of the main differences between the industrial and the academic landscapes of legal
tech should be the presence of not for profit projects in the academic sphere. We simply
list a few of these projects, noting that some that are called not for profit, seem to be for
profit, as well.
Non Profit Projects
CourtListener (Free Law Project, a California non-profit public benefit corporation) is
a free and open-source repository, search engine, and research platform for analysis of
court opinions and other legal documents. The system is a key set of metadata linked to
those opinions: a comprehensive database of judges and other persons involved in the
operation of the legal system. MassLegalHelp hopes to use the Web in creative ways to
improve access to justice for low income and disadvantaged people. They are working
to connect, support, and educate advocates and the general public. The content of the
website is written by people within the legal services community.
Caseloop (founded in 2015) is a free nationwide lawyer to lawyer directory matched
with referral tracking software. They aim to simplify the practice, reduce marketing
costs, build networks and boost lawyer’s income from referral fees. Caseloop allows attorneys
to list their firm information in their nationwide directory for free, refer cases to
each other, track their progress, and obtain referral fees.
Arbitrator Intelligence is a non-profit, interactive informational network that increases
and equalizes access to critical information in the arbitrator selection process. Arbitrator
Intelligences preliminary start-up phase, organized around a Pilot Project, concluded
on January 14, 2015. Arbitrator Intelligence plans to collect quantitative feedback
from users and counsel about key features of arbitrator decision making. Information will
be collected through surveys allowing users to provide feedback on specific questions
such as case management, evidence taking, and award rendering. When (and if) fully developed,
Arbitrator Intelligence will allow members to search accumulated information
to aid in their arbitrator selection process.
7. Conclusions
There is considerable criticism of researchers in Legal Informatics that they do not pay
enough attention to the pain points of the legal professionals and instead dedicate their
time and public funds to pursuing their philosophical leanings. To counteract this kind
of criticism we decided to survey the more applied landscape of legal technical startups
in the San Francisco Bay Area and to summarize our efforts, from the perspective
of researchers, interested in NLP, logics and ontologies, eager to apply our tools to real
life and actionable problems of lawyers and users of the law. While it is clear that in
the European Union, a big effort of consolidating laws and jurisprudence in different
languages, is a necessary first step, in the United States the multilinguality is not so
important. In the more applied side of the law in the US, the automation of documents
seems the main goal, followed closely by the automation of the practices workflow.
E-discovery is on the other hand the hard nut to crack and the one where the new
tools of AI might be very useful. eDiscovery software and services have become necessary
tools for enterprises aiming to resourcefully respond to legal, regulatory and investigational
requirements. Aggravated by ever-growing volumes of electronically stored
information (ESI), enterprises are facing mounting challenges in collection, review and
storing of digital data for use in litigation, regulatory and investigation processes. Massive
increases in electronic business-to-business (B2B) and business-to-consumer (B2C)
communication, growing use of smartphones, tablets and other Internet-enabled systems
for enterprise communication, and rising importance of big data in day-to-day business
operations are driving ESI volumes in enterprise environments. The data explosion is
creating challenges for IT and legal departments in all companies to efficiently handle
and manage data for use in active litigation and/or internal investigations. Enterprises of
all sizes, including large companies, multinational companies, and small and mediumsized
businesses (SMBs) are feeling the need for eDiscovery software and services. Additionally,
the growing number of criminal investigations and civil litigations embroiling
enterprise operations, along with the need for internal investigations for compliance and
regulatory purposes, will put more pressure on enterprises to prioritize deployment of
eDiscovery software and services.
References
[1] James E. Cabral, Abhijeet Chavan, Bonnie Rose Hough Linda Rexer Jane Ribadeneyra Thomas
M. Clarke, John Greacen, and Richard Zorza. Using technology to enhance access to justice. Harvard
Journal of Law and Technology, 26 (1)(243):29–pages, 2012.
[2] Commission on the future of legal services. Report on the Future of LEGAL SERVICES in the United
States. ABA, 2016.
[3] Hughes-Jehan Vibert, Pierre Jouvelot, and Benoˆıt Pin. Legivoc – Connecting Law in a Changing World.
Journal of Open Access to Law, 1(1):19–pages, 2013.


 

Fonte http://vcvpaiva.github.io/includes/pubs/2016-legal.pdf

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