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TLS0070 Introduction to
Legal Technology
Lecture 5 Applications I:
Information retrieval, knowledge
management, e-discovery
University of Turku Law School 2015-02-10
Anna Ronkainen @ronkaine
anna.ronkainen@onomatics.com
First a little digression (guess why...)
Google Flu Trends
-  predicting the timing and strength of
influenza epidemics based on the relative
frequency of certain keywords in searches
-  values for the model in black (dotted lines
95% confidence intervals for predicted
values), actual CDC influenza figures in red
But...
Performance after the initial period
Lessons worth learning (also for legal
applications)
-  transparency and replicability
-  use big data for understanding the unknown
-  study the algorithm
-  it’s not just about the size of the data
(from Lazer et al 2014)
Applications (general)
Application lectures overview
Applications I (this week):
-  information retrieval
-  e-discovery (e-disclosure)
-  knowledge management
Applications II (next week, 1st half):
-  case management
-  online dispute resolution
-  access to justice solutions
Applications III (next week, 2nd half):
-  decision support
-  prediction
-  automation
-  self-service
Legal tech applications not covered
here
-  general-purpose applications (like Office®/
office software)
-  legislative drafting applications
-  docket management (and other applications
for use within the judiciary)
-  courtroom visualization (etc.) software
-  ... and probably a ton of other things I don’t
even know existed
Information retrieval
Information retrieval (IR)
-  the granddaddy of legal tech applications
-  the only form of legal tech available in all
(industrial) countries at least in some form
-  making different types of static legal content
available for human consumption
-  statute law (+ commentaries)
-  case law
-  doctrine: journal articles and books
Information retrieval users
-  types of users:
-  lawyers in general
-  subgroups of lawyers (e.g. IP lawyers)
-  legal/admin support staff (e.g. tax
administrators, paralegals, informaticians)
-  other non-law professionals
-  ordinary citizens
-  different users have different needs in terms of
-  type and quantity of content required
-  terminology used
-  user interface in general
First-generation information retrieval
-  take whatever text you have (on paper) and
put it into a database
-  full-text search (exact match or wildcards)
-  structured search (in whatever fields are
available)
-  Boolean search with AND, OR, NOT
-  some metadata enhancements like keywords
(typically same as on paper)
Present-day Boolean search example:
TMview
Further developments
-  hypertext (links)
-  better search capabilities with language
technology (try searching for “back” as a
noun)
-  relevancy ranking
-  recommendations for further reading
-  morebetter metadata
An example: WestlawNext
-  natural-language and Boolean search
-  relevancy ranking of sources of law, using
(among others) a network of links between
cases
-  (commercial break, text version:
http://info.legalsolutions.thomsonreuters.com/pdf/wln2/L-355700_v2.pdf)
On the horizon
-  natural-language query interfaces and
advanced text understanding (think Watson/
Siri)
-  merging relevancy ranking with predictive
legal analytics (like a certain trademark
platform)
-  even more polarization between biggest
markets (esp. US) and others (e.g. Finland,
let alone developing countries)
Knowledge management
Knowledge management
-  taking (and improving upon!) the knowledge
(explicit and tacit!) of an organization and
putting it into optimal use
-  by no means just tech: creating and developing
processes within the organization is equally
important
-  can take different forms:
-  internal: e.g. making work product (memos,
contracts etc.) electronically searchable
-  external: creating digital legal content for use
by law firm customers
Knowledge management advantages
-  higher efficiency -> better service
-  higher quality (better dissemination of
expertise)
-  makes life easier for lawyers (increased
productivity, reduced stress)
-  keeps knowledge in the firm even if individuals
leave
-  helps with the training of new lawyers
-  necessary for good risk management
(after Kay 2003)
One knowledge management example:
contract management
-  the default solution that’s still used by many
(most?) companies: paper + binders
-  low overhead; manageable with low volumes
-  doesn’t scale (cope with large volumes) well,
e.g. finding information becomes difficult
-  particularly kludgy when documents needed
externally (due diligence, anyone?)
-  error-prone and fragile
-  still need to manage templates somewhere
(lack of central storage leads to inconsistencies)
Low-tech electronic contract
management
-  establish a central organization-wide repository for
signed contracts and official templates
-  doesn’t need proprietary software, any LAN or
cloud based (private) file sharing solution works
-  electronically searchable, at least if word processing
documents and scans are kept together
-  works well (enough) if there are good processes
(e.g. regarding file naming and organization of files)
and they are (always!) consistently adhered to
-  ...which this solution obviously cannot enforce
-  no built-in workflow management
Dedicated contract lifecycle
management (CLM) solutions
-  hundreds of providers, including two from Finland (that I
know of: M-Files and Sopima)
-  functionalities of varying sophistication for different stages
in the contract lifecycle:
-  contract and clause template libraries
-  platform and history for internal review
-  platform and history for negotiations and external
review
-  electronic signing / import of scanned definitive paper
originals
-  archiving, retrieval etc.
-  workflow management, managing access privileges etc.
Exhibit A: Sopima
Exhibit B: M-Files
https://www.youtube.com/watch?v=0b0xSVHOFIg
Electronic signing
-  real electronic signing not widespread
(outside Estonia, anyway), to a great deal
due to a lack of standards internationally
(and esp. for identifying legal persons)
-  pseudo-electronic signing (images manually
written signatures stored electronically) now
quite widespread, dedicated solutions and
support in CLM systems also available
-  the latter raises some obvious questions
about probative value
Heck, even Apple does it:
In summary: Levels of contract
management adoption
(via Juntunen 2013)
Another knowledge management
example: Fondia’s Virtual Lawyer
Fondia’s Virtual Lawyer
-  a collection of ~1700 short documents made
by Fondia staff describing the legal aspects
of particular situations
-  for external use (self-help by Fondia clients
etc.), AFAIK also used internally in an
enhanced version
-  not for total novices
-  available at virtuallawyer.fi for free,
registration required, document template
library additionally available for a fee
Electronic discovery
(disclosure)
Discovery in electronically stored
information (e-discovery)
-  emerged out of nowhere a dozen years ago
-  now a multi-billion-dollar industry (mostly US),
hundreds of providers
-  roots in more general-purpose language tech
(outside the AI & law community)
-  Enron corpus, Sedona Conference, TREC, DESI
-  storage requirements for e-mail etc. introduced
(US) by amendments to Federal Rules of Civil
Procedure in 2006
...and now* it’s already this much
widespread (in the US, anyway):
*: actually this book is from 2009
Zubulake v. UBS Warburg
-  employment law case in District Court for
Southern NY, heard 2003–2005
-  led to four groundbreaking rulings which set
the basic standards for e-discovery (before
2006 FRCP revisions), widely referred to as
Zubulake I, III, IV, V
Zubulake I and III
-  what data is considered accessible ESI
-  yes: online data/hard disks, optical disks, offline magnetic tapes
-  no: backup tapes, damaged/deleted/... data
-  no -> yes if considerable evidentiary value can be demonstrated, for
which a 7-factor test was introduced:
-  The extent to which the request is specifically tailored to discover
relevant information;
-  The availability of such information from other sources;
-  The total cost of production, compared to the amount in
controversy;
-  The total cost of production, compared to the resources available to
each party;
-  The relative ability of each party to control costs and its incentive to
do so;
-  The importance of the issues at stake in the litigation; and
-  The relative benefits to the parties of obtaining the information.
Zubulake IV
-  some backups no longer available
-  relevant emails (created after the start of the
proceedings) had been deleted
-  defendant had a duty to preserve evidence
(since relevant for ongoing/future litigation)
-  plaintiff got access to the information
-  however, plaintiff couldn’t show adverse
interference (at this stage) and was ordered
to pay the costs
Zubulake V
-  upon the plaintiff’s motion, the court
concluded that the defendant (and defence
counsel) had failed to safeguard and produce
evidence in an adequate manner
-  defendant sanctioned and ordered to pay
plaintiff’s costs for producing evidence
(witness re-examination etc.) necessary due
to plaintiff’s late (or non-)production of
relevant evidence
Outcome
-  active interference (intentional destruction
or hiding of evidence) ruled by the judge
-  jury found in favour of the plaintiff,
compensatory and punitive damages
-  reimbursement of even more costs to the
plaintiff (generally a lot more unusual in US)
E-discovery workflow
-  establish an ESI retention policy, stick to it when
creating and storing data
-  identify relevant ESI, create authentic snapshot and
collect it for further processing
-  process and filter ESI (e.g. removal of duplicates)
-  review and analyze ESI for privileged information
-  produce ESI after filtering out irrelevant, duplicated
or privileged materials
-  possibly clawback if too much produced in error
-  present at trial (if it ever goes that far)
First-generation e-discovery
-  based on lists of specific search terms (or
phrases) proposed by the plaintiff and
approved or modified by the judge
-  a bit sketchy, not even real consensus about
whether keywords cover all inflections?
-  no longer considered acceptable by many of
the most influential US judges for this field
Predictive coding
-  based on coding a (very) small subset of the
relevant document mass as responsive or not
(should/n’t be released)
-  then using that as the teaching set for a
machine learning algorithm
-  performance comparable to (or better than)
human reviewers at a fraction of the cost
E-discovery output
-  native (original) formats (e.g.: .docx)
-  usually better for the plaintiff: electronically
searchable
-  native file formats for proprietary software not
necessarily openable without that software
-  “petrified” formats (tiff, pdf)
-  often better for the defendant: almost the same
as handing out the data on paper
-  general-purpose tools enough for viewing
-  easier to redact
What’s the status with e-discovery
-  very widespread in the US (because it’s the
law!)
-  gaining popularity in the rest of Anglophonia
(because common law; tech readily available for
English)
-  some providers also support major European
and Asian languages (mostly for international
companies operating in the US)
-  rest of the world: is there even a word for this?
(then again: discovery in the common-law sense
doesn’t exist in most civil-law countries (incl.
Finland) in general)
No concrete examples
-  (because, frankly, I understand neither the field
nor the legal issue well enough)
-  but e-discovery in itself is an interesting
example of legal tech for many reasons
-  first real big data application for law
-  came out of nowhere in the early 2000s
-  now a multi-billion-dollar industry (US)
-  many startups, some notable exits (e.g.
Cataphora’s e-discovery ops to EY)
-  also continuously new funding rounds (even
$100M+) to more and more companies
Questions?

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Introduction to Legal Technology, lecture 5 (2015)

  • 1. TLS0070 Introduction to Legal Technology Lecture 5 Applications I: Information retrieval, knowledge management, e-discovery University of Turku Law School 2015-02-10 Anna Ronkainen @ronkaine anna.ronkainen@onomatics.com
  • 2. First a little digression (guess why...)
  • 3. Google Flu Trends -  predicting the timing and strength of influenza epidemics based on the relative frequency of certain keywords in searches -  values for the model in black (dotted lines 95% confidence intervals for predicted values), actual CDC influenza figures in red
  • 4.
  • 6. Performance after the initial period
  • 7. Lessons worth learning (also for legal applications) -  transparency and replicability -  use big data for understanding the unknown -  study the algorithm -  it’s not just about the size of the data (from Lazer et al 2014)
  • 9. Application lectures overview Applications I (this week): -  information retrieval -  e-discovery (e-disclosure) -  knowledge management Applications II (next week, 1st half): -  case management -  online dispute resolution -  access to justice solutions Applications III (next week, 2nd half): -  decision support -  prediction -  automation -  self-service
  • 10. Legal tech applications not covered here -  general-purpose applications (like Office®/ office software) -  legislative drafting applications -  docket management (and other applications for use within the judiciary) -  courtroom visualization (etc.) software -  ... and probably a ton of other things I don’t even know existed
  • 12. Information retrieval (IR) -  the granddaddy of legal tech applications -  the only form of legal tech available in all (industrial) countries at least in some form -  making different types of static legal content available for human consumption -  statute law (+ commentaries) -  case law -  doctrine: journal articles and books
  • 13. Information retrieval users -  types of users: -  lawyers in general -  subgroups of lawyers (e.g. IP lawyers) -  legal/admin support staff (e.g. tax administrators, paralegals, informaticians) -  other non-law professionals -  ordinary citizens -  different users have different needs in terms of -  type and quantity of content required -  terminology used -  user interface in general
  • 14. First-generation information retrieval -  take whatever text you have (on paper) and put it into a database -  full-text search (exact match or wildcards) -  structured search (in whatever fields are available) -  Boolean search with AND, OR, NOT -  some metadata enhancements like keywords (typically same as on paper)
  • 15. Present-day Boolean search example: TMview
  • 16. Further developments -  hypertext (links) -  better search capabilities with language technology (try searching for “back” as a noun) -  relevancy ranking -  recommendations for further reading -  morebetter metadata
  • 17. An example: WestlawNext -  natural-language and Boolean search -  relevancy ranking of sources of law, using (among others) a network of links between cases -  (commercial break, text version: http://info.legalsolutions.thomsonreuters.com/pdf/wln2/L-355700_v2.pdf)
  • 18. On the horizon -  natural-language query interfaces and advanced text understanding (think Watson/ Siri) -  merging relevancy ranking with predictive legal analytics (like a certain trademark platform) -  even more polarization between biggest markets (esp. US) and others (e.g. Finland, let alone developing countries)
  • 20. Knowledge management -  taking (and improving upon!) the knowledge (explicit and tacit!) of an organization and putting it into optimal use -  by no means just tech: creating and developing processes within the organization is equally important -  can take different forms: -  internal: e.g. making work product (memos, contracts etc.) electronically searchable -  external: creating digital legal content for use by law firm customers
  • 21. Knowledge management advantages -  higher efficiency -> better service -  higher quality (better dissemination of expertise) -  makes life easier for lawyers (increased productivity, reduced stress) -  keeps knowledge in the firm even if individuals leave -  helps with the training of new lawyers -  necessary for good risk management (after Kay 2003)
  • 22. One knowledge management example: contract management -  the default solution that’s still used by many (most?) companies: paper + binders -  low overhead; manageable with low volumes -  doesn’t scale (cope with large volumes) well, e.g. finding information becomes difficult -  particularly kludgy when documents needed externally (due diligence, anyone?) -  error-prone and fragile -  still need to manage templates somewhere (lack of central storage leads to inconsistencies)
  • 23. Low-tech electronic contract management -  establish a central organization-wide repository for signed contracts and official templates -  doesn’t need proprietary software, any LAN or cloud based (private) file sharing solution works -  electronically searchable, at least if word processing documents and scans are kept together -  works well (enough) if there are good processes (e.g. regarding file naming and organization of files) and they are (always!) consistently adhered to -  ...which this solution obviously cannot enforce -  no built-in workflow management
  • 24. Dedicated contract lifecycle management (CLM) solutions -  hundreds of providers, including two from Finland (that I know of: M-Files and Sopima) -  functionalities of varying sophistication for different stages in the contract lifecycle: -  contract and clause template libraries -  platform and history for internal review -  platform and history for negotiations and external review -  electronic signing / import of scanned definitive paper originals -  archiving, retrieval etc. -  workflow management, managing access privileges etc.
  • 27. Electronic signing -  real electronic signing not widespread (outside Estonia, anyway), to a great deal due to a lack of standards internationally (and esp. for identifying legal persons) -  pseudo-electronic signing (images manually written signatures stored electronically) now quite widespread, dedicated solutions and support in CLM systems also available -  the latter raises some obvious questions about probative value
  • 28. Heck, even Apple does it:
  • 29. In summary: Levels of contract management adoption (via Juntunen 2013)
  • 30. Another knowledge management example: Fondia’s Virtual Lawyer
  • 31.
  • 32. Fondia’s Virtual Lawyer -  a collection of ~1700 short documents made by Fondia staff describing the legal aspects of particular situations -  for external use (self-help by Fondia clients etc.), AFAIK also used internally in an enhanced version -  not for total novices -  available at virtuallawyer.fi for free, registration required, document template library additionally available for a fee
  • 34. Discovery in electronically stored information (e-discovery) -  emerged out of nowhere a dozen years ago -  now a multi-billion-dollar industry (mostly US), hundreds of providers -  roots in more general-purpose language tech (outside the AI & law community) -  Enron corpus, Sedona Conference, TREC, DESI -  storage requirements for e-mail etc. introduced (US) by amendments to Federal Rules of Civil Procedure in 2006
  • 35. ...and now* it’s already this much widespread (in the US, anyway): *: actually this book is from 2009
  • 36. Zubulake v. UBS Warburg -  employment law case in District Court for Southern NY, heard 2003–2005 -  led to four groundbreaking rulings which set the basic standards for e-discovery (before 2006 FRCP revisions), widely referred to as Zubulake I, III, IV, V
  • 37. Zubulake I and III -  what data is considered accessible ESI -  yes: online data/hard disks, optical disks, offline magnetic tapes -  no: backup tapes, damaged/deleted/... data -  no -> yes if considerable evidentiary value can be demonstrated, for which a 7-factor test was introduced: -  The extent to which the request is specifically tailored to discover relevant information; -  The availability of such information from other sources; -  The total cost of production, compared to the amount in controversy; -  The total cost of production, compared to the resources available to each party; -  The relative ability of each party to control costs and its incentive to do so; -  The importance of the issues at stake in the litigation; and -  The relative benefits to the parties of obtaining the information.
  • 38. Zubulake IV -  some backups no longer available -  relevant emails (created after the start of the proceedings) had been deleted -  defendant had a duty to preserve evidence (since relevant for ongoing/future litigation) -  plaintiff got access to the information -  however, plaintiff couldn’t show adverse interference (at this stage) and was ordered to pay the costs
  • 39. Zubulake V -  upon the plaintiff’s motion, the court concluded that the defendant (and defence counsel) had failed to safeguard and produce evidence in an adequate manner -  defendant sanctioned and ordered to pay plaintiff’s costs for producing evidence (witness re-examination etc.) necessary due to plaintiff’s late (or non-)production of relevant evidence
  • 40. Outcome -  active interference (intentional destruction or hiding of evidence) ruled by the judge -  jury found in favour of the plaintiff, compensatory and punitive damages -  reimbursement of even more costs to the plaintiff (generally a lot more unusual in US)
  • 41. E-discovery workflow -  establish an ESI retention policy, stick to it when creating and storing data -  identify relevant ESI, create authentic snapshot and collect it for further processing -  process and filter ESI (e.g. removal of duplicates) -  review and analyze ESI for privileged information -  produce ESI after filtering out irrelevant, duplicated or privileged materials -  possibly clawback if too much produced in error -  present at trial (if it ever goes that far)
  • 42. First-generation e-discovery -  based on lists of specific search terms (or phrases) proposed by the plaintiff and approved or modified by the judge -  a bit sketchy, not even real consensus about whether keywords cover all inflections? -  no longer considered acceptable by many of the most influential US judges for this field
  • 43. Predictive coding -  based on coding a (very) small subset of the relevant document mass as responsive or not (should/n’t be released) -  then using that as the teaching set for a machine learning algorithm -  performance comparable to (or better than) human reviewers at a fraction of the cost
  • 44. E-discovery output -  native (original) formats (e.g.: .docx) -  usually better for the plaintiff: electronically searchable -  native file formats for proprietary software not necessarily openable without that software -  “petrified” formats (tiff, pdf) -  often better for the defendant: almost the same as handing out the data on paper -  general-purpose tools enough for viewing -  easier to redact
  • 45. What’s the status with e-discovery -  very widespread in the US (because it’s the law!) -  gaining popularity in the rest of Anglophonia (because common law; tech readily available for English) -  some providers also support major European and Asian languages (mostly for international companies operating in the US) -  rest of the world: is there even a word for this? (then again: discovery in the common-law sense doesn’t exist in most civil-law countries (incl. Finland) in general)
  • 46. No concrete examples -  (because, frankly, I understand neither the field nor the legal issue well enough) -  but e-discovery in itself is an interesting example of legal tech for many reasons -  first real big data application for law -  came out of nowhere in the early 2000s -  now a multi-billion-dollar industry (US) -  many startups, some notable exits (e.g. Cataphora’s e-discovery ops to EY) -  also continuously new funding rounds (even $100M+) to more and more companies