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From Research to Innovative
Legal Tech Products
Anna Ronkainen
Chief Scientist + co-founder, TrademarkNow
Executive committee member, IAAIL
President, Finnish Legal Tech Forum
@ronkaine
Tallinn 2016-05-26
What technology has had the
biggest impact on law?
Writing!
Code	of	Hammurabi,	
Mesopotamia	1754	BCE	
(Wikimedia:	Rama	CC	BY-SA	2.0)
The printing press probably
comes a close second
Law has no problem with technology –
at least some of the time
U 1959.40 H:
The Danish Supreme Court
said it’s ok to apply for
recording a deed using a
document produced with a
novel technology – the
ballpoint pen!
On to the computer age...
Law as a true IT pioneer:
The first search-and-replace ever
-  terminology change in the Pennsylvania health
code in the late 1950s:
s/retarded child/exceptional child/g
-  legislative technique required all instances of
textual changes to be enumerated individually
-  the legislature turned to prof Horty at Penn
-  first tried to solve this manually, too unreliable
-  solution: input text into computer, index the
position of each word to find all occurrences of the
word in question
-  obviously generalizable into textual information
retrieval in general
Next steps
-  M.U.L.L. (later Jurimetrics) journal 1959–
-  case law retrieval experiments by Colin Tapper
(Oxford) through the 1960s
-  Centre d’études pour le traitement de
l'information juridique (IRETIJ, Montpellier)
1965
-  CREDOC (Belgium) 1967
-  OBAR (Ohio) 1964 -> LEXIS 1970
-  NORIS (Norway) 1970
-  Westlaw 1975
First expert systems: mid-1980s
-  inspired by systems from other fields (e.g.
MYCIN)
-  Latent Damage Law (Susskind and Capper)
-  British Nationality Act (Bench-Capon and
Sergot)
-  SHYSTER (Popple)
Institutionalization: 1980s
-  scholarly societies and the conferences they
sponsor start: Stichting voor juridische
kennissystemen + JURIX (initially NL/BE);
International Association/Conference for
Artificial Intelligence and Law (IAAIL/ICAIL)
-  AI & Law journal (published by Kluwer/
Springer)
-  legal informatics firmly established as a field
of legal education, or so it seemed...
Where did all the lawyers go?
-  the PC revolution (1980s) and the launch of
the commercial Internet (1993) ->
computer-related legal problems
everywhere!
-  expert systems were considered a failure –
not just in law – for good reason -> the AI
winter of late 1980s
-  leaving the field to computer scientists and
legal theorists made AI & law less relevant
for practice innovations
E-discovery: a wake-up call
-  taking general natural language processing
tech to solve the legal issue of document
review (predictive coding)
-  became into existence less than 15 years
ago, now a multi-billion-dollar industry
-  in some cases oldskool review no longer
acceptable
-  (solves a legal problem that mostly only
exists in common-law countries...)
Legal startups – law’s R&D department
-  10 years ago: maybe 20 legal startups total
-  now: 600–1200 legal startups (depending on
definition and who you ask)
-  approx 2/3 of legal startups are from the US!
-  for more information, see AngelList or ...
http://tech.law.stanford.edu/
What does this mean for legal
practice?
200–300B USD/year!
The Susskind model of professional
services evolution
From artisanal
(“bespoke”) lawyering
through
standardization and
systematization to
commoditized legal
services (with no
human involvement)
How does the average lawyer seem to
feel about all this?
What is already out there?
*
* for CALIFORNIA
INSOLVENCY LAW
Watson is not the only machine-
learning-as-a-service service out there
Watson may be a closely-guarded secret, but
open-source machine learning tool kits are a
big trend in 2015–16:
-  Google: TensorFlow, SyntaxNet
-  Facebook: Torch
-  Amazon Machine Learning
-  etc etc etc
For legal, it’s mostly just a set of bold
promises, unlike...
The Civil Resolution Tribunal, British
Columbia (Canada)
-  small-claims court online, starting in 2016
-  1st step: online resources for identifying and
resolving your legal problems yourself
-  2nd step: online mediation between the
parties
-  3rd step: court trial carried out entirely
online
-  similar system proposed in England (HMOC)
What about research?
Research commercialization is difficult
in general – not only for AI & law
-  innovation and commercialization are tossed
around as vital research policy goals a lot these
days pretty much wherever you go
-  said tossers* tend to treat it as a black box,
basically thinking that telling academics to be
innovative is all it takes
-  there are two parts in the equation, and only
one of them can be said to be the academics’
responsibility
* sorry, couldn’t resist
Why research commercialization fails
-  most such ventures fail for a simple reason: putting the
cart before the horse
-  solution looking for a problem, not the other way
around
-  academics (typically) don’t have a very commercially
oriented mindset
-  perhaps most importantly, product design and
management are often left out of the equation
altogether
-  basic research is a fairly blunt instrument: research end-
product (good enough for publication) very different
from a marketable and commercially viable product
The first part of the equation:
What academics can do about it
-  consider potential uses even when planning
and carrying out basic research
-  and of course there’s also applied research:
for legal tech, a lot of general AI/NLP stuff
just waiting to be (tried out to see if it can
be) used (cf. e-discovery)
-  try to take an active role in seeking out
potential partners for commercialization (no
time for that, I know...)
Applied and basic research:
Pasteur’s quadrant
Quest for
fundamental
understanding? yes
Pure basic
research
(Bohr)
Use-inspired
basic research
(Pasteur)
no
-
Pure applied
research
(Edison)
no yes
Considerations of use?
(Stokes 1997)
The other part of the equation:
The people with the actual problems
-  you are more likely to end up with a viable
product when you start with a problem and
use research to look for a solution, not the
other way around
-  the initiative should come from someone
who has experienced the pain points first
hand – or at least people who can see an
inefficiency, have an idea about what to do
about it, and can figure out how to fill in the
blanks
Exhibit A: LexMachina
-  started as a research project (IP Litigation
Clearinghouse) between Stanford Law and
CS departments
-  spun off as an independent company in
2008
-  initial university funding $3M, seed $2M,
series A $4.8M (according to Wikipedia)
-  sold to LexisNexis in 2015
Exhibit B: TrademarkNow
-  started(1) as a research project into
computational modelling of vague legal
concepts, using trademark likelihood of
confusion as a random example
-  started(2) as one frustrated trademark
lawyer’s quest for better tools
-  incorporated in 2012, 4 co-founders (3 tech,
2 legal)
-  funding: $300k seed, $3M series A, plus
gov’t innovation funding (Tekes)
Exhibit C: Riverview Law
-  one of the biggest ABSs in the UK
-  started a collaboration with University of
Liverpool in January 2015
-  Riverview pays for two(?) researchers to
work on figuring out how to use the legal AI
research from Liverpool (esp. strong on legal
argumentation) to solve business problems
-  success? Too early to tell yet...
Exhibit D: Dutch government agencies
-  e.g. Tax and Customs Administration and
Immigration and Naturalization Service
(IND)
-  leveraging the strong Dutch AI & law
community by using university researchers in
consulting roles
-  development work done by the usual
external suppliers (Accenture etc.)
-  e.g. INDIGO: http://vimeo.com/43187024
Oh, and don’t forget education
-  tech will be a bigger part of most lawyers’ day-
to-day work
-  new types of emerging legal careers where deep
tech/design skills also needed (but you still
need the deep legal expertise as well)
-  legal tech/entrepreneurship programs
established in many US universities, first EU
ones just getting started
-  I taught Intro to Legal Tech in Turku in 2015
(and again later this year), do take a look and
feel free to borrow...
What about Estonia?
Do I even have to say it...?
(CodeX FutureLaw 2016 conference
at Stanford, panel on e-government)
Why Estonia
-  strong track record of doing things right in
public sector IT
-  existing legal AI research base
-  small country: easier to be agile
-  small market: nobody from the outside is
going to come and build it for you
-  strong and competitive startup/tech scene,
well positioned for many major markets in
Europe
What more do you need?
-  innovative lawyers to start using (and
helping to create!) new technologies
-  willingness from the courts (and the bar) to
try new things
-  startups, startups, startups!
-  introduce legal tech in legal education
-  and of course targeted research funding
wouldn’t hurt, either
Thank you!
More information:
@ronkaine on Twitter
http://slideshare.net/ronkaine
https://www.trademarknow.com/
http://iaail.org/

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From Research to Innovative Legal Tech Products

  • 1. From Research to Innovative Legal Tech Products Anna Ronkainen Chief Scientist + co-founder, TrademarkNow Executive committee member, IAAIL President, Finnish Legal Tech Forum @ronkaine Tallinn 2016-05-26
  • 2. What technology has had the biggest impact on law?
  • 4. The printing press probably comes a close second
  • 5. Law has no problem with technology – at least some of the time U 1959.40 H: The Danish Supreme Court said it’s ok to apply for recording a deed using a document produced with a novel technology – the ballpoint pen!
  • 6. On to the computer age...
  • 7. Law as a true IT pioneer: The first search-and-replace ever -  terminology change in the Pennsylvania health code in the late 1950s: s/retarded child/exceptional child/g -  legislative technique required all instances of textual changes to be enumerated individually -  the legislature turned to prof Horty at Penn -  first tried to solve this manually, too unreliable -  solution: input text into computer, index the position of each word to find all occurrences of the word in question -  obviously generalizable into textual information retrieval in general
  • 8. Next steps -  M.U.L.L. (later Jurimetrics) journal 1959– -  case law retrieval experiments by Colin Tapper (Oxford) through the 1960s -  Centre d’études pour le traitement de l'information juridique (IRETIJ, Montpellier) 1965 -  CREDOC (Belgium) 1967 -  OBAR (Ohio) 1964 -> LEXIS 1970 -  NORIS (Norway) 1970 -  Westlaw 1975
  • 9. First expert systems: mid-1980s -  inspired by systems from other fields (e.g. MYCIN) -  Latent Damage Law (Susskind and Capper) -  British Nationality Act (Bench-Capon and Sergot) -  SHYSTER (Popple)
  • 10. Institutionalization: 1980s -  scholarly societies and the conferences they sponsor start: Stichting voor juridische kennissystemen + JURIX (initially NL/BE); International Association/Conference for Artificial Intelligence and Law (IAAIL/ICAIL) -  AI & Law journal (published by Kluwer/ Springer) -  legal informatics firmly established as a field of legal education, or so it seemed...
  • 11. Where did all the lawyers go? -  the PC revolution (1980s) and the launch of the commercial Internet (1993) -> computer-related legal problems everywhere! -  expert systems were considered a failure – not just in law – for good reason -> the AI winter of late 1980s -  leaving the field to computer scientists and legal theorists made AI & law less relevant for practice innovations
  • 12. E-discovery: a wake-up call -  taking general natural language processing tech to solve the legal issue of document review (predictive coding) -  became into existence less than 15 years ago, now a multi-billion-dollar industry -  in some cases oldskool review no longer acceptable -  (solves a legal problem that mostly only exists in common-law countries...)
  • 13. Legal startups – law’s R&D department -  10 years ago: maybe 20 legal startups total -  now: 600–1200 legal startups (depending on definition and who you ask) -  approx 2/3 of legal startups are from the US! -  for more information, see AngelList or ...
  • 15. What does this mean for legal practice?
  • 16.
  • 17.
  • 19. The Susskind model of professional services evolution From artisanal (“bespoke”) lawyering through standardization and systematization to commoditized legal services (with no human involvement)
  • 20. How does the average lawyer seem to feel about all this?
  • 21. What is already out there?
  • 22.
  • 23.
  • 25.
  • 26.
  • 27. Watson is not the only machine- learning-as-a-service service out there Watson may be a closely-guarded secret, but open-source machine learning tool kits are a big trend in 2015–16: -  Google: TensorFlow, SyntaxNet -  Facebook: Torch -  Amazon Machine Learning -  etc etc etc For legal, it’s mostly just a set of bold promises, unlike...
  • 28.
  • 29.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36.
  • 37.
  • 38.
  • 39.
  • 40. The Civil Resolution Tribunal, British Columbia (Canada) -  small-claims court online, starting in 2016 -  1st step: online resources for identifying and resolving your legal problems yourself -  2nd step: online mediation between the parties -  3rd step: court trial carried out entirely online -  similar system proposed in England (HMOC)
  • 42. Research commercialization is difficult in general – not only for AI & law -  innovation and commercialization are tossed around as vital research policy goals a lot these days pretty much wherever you go -  said tossers* tend to treat it as a black box, basically thinking that telling academics to be innovative is all it takes -  there are two parts in the equation, and only one of them can be said to be the academics’ responsibility * sorry, couldn’t resist
  • 43. Why research commercialization fails -  most such ventures fail for a simple reason: putting the cart before the horse -  solution looking for a problem, not the other way around -  academics (typically) don’t have a very commercially oriented mindset -  perhaps most importantly, product design and management are often left out of the equation altogether -  basic research is a fairly blunt instrument: research end- product (good enough for publication) very different from a marketable and commercially viable product
  • 44. The first part of the equation: What academics can do about it -  consider potential uses even when planning and carrying out basic research -  and of course there’s also applied research: for legal tech, a lot of general AI/NLP stuff just waiting to be (tried out to see if it can be) used (cf. e-discovery) -  try to take an active role in seeking out potential partners for commercialization (no time for that, I know...)
  • 45. Applied and basic research: Pasteur’s quadrant Quest for fundamental understanding? yes Pure basic research (Bohr) Use-inspired basic research (Pasteur) no - Pure applied research (Edison) no yes Considerations of use? (Stokes 1997)
  • 46. The other part of the equation: The people with the actual problems -  you are more likely to end up with a viable product when you start with a problem and use research to look for a solution, not the other way around -  the initiative should come from someone who has experienced the pain points first hand – or at least people who can see an inefficiency, have an idea about what to do about it, and can figure out how to fill in the blanks
  • 47. Exhibit A: LexMachina -  started as a research project (IP Litigation Clearinghouse) between Stanford Law and CS departments -  spun off as an independent company in 2008 -  initial university funding $3M, seed $2M, series A $4.8M (according to Wikipedia) -  sold to LexisNexis in 2015
  • 48. Exhibit B: TrademarkNow -  started(1) as a research project into computational modelling of vague legal concepts, using trademark likelihood of confusion as a random example -  started(2) as one frustrated trademark lawyer’s quest for better tools -  incorporated in 2012, 4 co-founders (3 tech, 2 legal) -  funding: $300k seed, $3M series A, plus gov’t innovation funding (Tekes)
  • 49. Exhibit C: Riverview Law -  one of the biggest ABSs in the UK -  started a collaboration with University of Liverpool in January 2015 -  Riverview pays for two(?) researchers to work on figuring out how to use the legal AI research from Liverpool (esp. strong on legal argumentation) to solve business problems -  success? Too early to tell yet...
  • 50. Exhibit D: Dutch government agencies -  e.g. Tax and Customs Administration and Immigration and Naturalization Service (IND) -  leveraging the strong Dutch AI & law community by using university researchers in consulting roles -  development work done by the usual external suppliers (Accenture etc.) -  e.g. INDIGO: http://vimeo.com/43187024
  • 51. Oh, and don’t forget education -  tech will be a bigger part of most lawyers’ day- to-day work -  new types of emerging legal careers where deep tech/design skills also needed (but you still need the deep legal expertise as well) -  legal tech/entrepreneurship programs established in many US universities, first EU ones just getting started -  I taught Intro to Legal Tech in Turku in 2015 (and again later this year), do take a look and feel free to borrow...
  • 53. Do I even have to say it...? (CodeX FutureLaw 2016 conference at Stanford, panel on e-government)
  • 54. Why Estonia -  strong track record of doing things right in public sector IT -  existing legal AI research base -  small country: easier to be agile -  small market: nobody from the outside is going to come and build it for you -  strong and competitive startup/tech scene, well positioned for many major markets in Europe
  • 55. What more do you need? -  innovative lawyers to start using (and helping to create!) new technologies -  willingness from the courts (and the bar) to try new things -  startups, startups, startups! -  introduce legal tech in legal education -  and of course targeted research funding wouldn’t hurt, either
  • 56. Thank you! More information: @ronkaine on Twitter http://slideshare.net/ronkaine https://www.trademarknow.com/ http://iaail.org/