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TLS0070 Introduction to
Legal Technology
Lecture 8
Regulatory issues
University of Turku Law School 2015-02-24
Anna Ronkainen @ronkaine
anna.ronkainen@onomatics.com
But first...
Final paper
-  2500–4000 words (10–16 pages), more does
not necessarily imply better!
-  to be returned on Moodle by Fri Apr 10, use
pdf as file format
-  will do my best to grade the papers within 2
weeks after the deadline
-  normal academic style, use references (in-
text, footnotes, endnotes – whatever you are
used to but just be consistent) to document
the sources you have used in your work
Topic and form
-  must be approved by the lecturer in advance (before/after
lectures or via e-mail) by end of March at the latest
-  possible topics (non-exhaustive list):
-  some specific technology and its application to law (in
general or to a specific field)
-  some specific field of law or type/stage of legal practice
and the current/potential application of technology in it
(in general or specific)
-  a specific legal/regulatory issue related to the use of
technology by lawyers or for things normally done by
lawyers
-  thorough analysis of 1–2 existing legal startup(s)
-  business plan for your own future legal startup
Specifically for the startup-type topics
-  include at least the following
-  basic facts about the organization: age, legal structure,
geography, founders, funding etc.
-  what legal problem they are trying to solve
-  competition (including less techy alternatives)
-  business model (NB: non-profits are okay too!)
-  description of the technologies used
-  this is not business school so I don’t expect you to be an
expert in that – look at it from a lawyer’s perspective
-  commercial viability is a part of the grading criteria if you
write about your own imaginary startup
-  startup people generally love talking about their work so
feel free to try to contact them (but only one paper
allowed per startup (and none about TrademarkNow))
Cloud computing –
friend or foe?
What is cloud computing?
-  remote servers and networks allowing for
centralized data storage and online access to
services
-  SaaS: software as a service
-  PaaS: platform as a service
-  IaaS: infrastructure as a service
-  it’s not exactly new: pre-PC mainframes
(including Westlaw/LexisNexis) were also “in
the cloud”
What’s wrong with it
-  it’s new and different
-  trust issues wrt cloud service provider
-  data protection complications with non-EU
cloud providers as data processors
-  requires good net connectivity
-  risk of eavesdropping
On the other side
-  cheaper/easier to manage (economies of scale)
-  same data and services available across different
platforms
-  many types of modern technology only
available on the cloud
-  usually you can get an EU provider if that’s
important from a data protection perspective
-  usually the weakest link in terms of security is
somewhere between the keyboard and the
chair
Data protection, big data,
and automated decisions
An example: behavioural biometrics
What are biometrics?
- the measurement of anatomical, physiological,
chemical or behavioural characteristics of an
individual
- according to the traditional definition only used
for the purposes of recognition, verification or
identification of a given individual human being
(probably mostly because that’s all what the
technology could be used for earlier)
- e.g fingerprints, iris scans, face recognition, voice
patterns, gait patterns, keystroke timing...
Biometrics in law
- a well-established feature of data
protection law for several decades already
- special because based on (mostly)
immutable characteristics of a given
individual: you can’t swap your irises the
way you’d change a password
-  but that’s just the beginning...
Dynamic behavioural biometrics
(or biomarkers) (for lack of a better term)
- behavioural biometrics: based on acquired
individual traits rather than innate features
- dynamic: gathered over a longer time
interval (usually at least minutes and in a
specific context)
- can be used for much more than just
identification with modern technology
- the measurements are not (only) used to
compute an identification key but contain
other types of (even sensitive) personal data
as well
State of the art?
A simple example: heart rate monitoring
from to...
State of the art
On to mind-reading
- considerable interest in the use of neuroscience as
legal evidence
- including functional neuroimaging: a behavioural
biometric
- the use of fMRI lie detection not yet allowed in
the US (attempts at least in Tennessee, New York,
Maryland), EEG-based ”lie detection” allowed in
Sharma case in Pune, India in 2008 (despite
massive criticism by neuroscientists)
http://www.nytimes.com/2008/09/15/world/asia/15iht-15brainscan.16148673.html
fMRI mind-reading:
not very subtle
Oldskool lie detection:
the polygraph
EEG: a more discreet (and crackable)
example
(emotiv.com)
But what about this?
AVATAR: Automated Virtual Agent for
Truth Assessment in Real-Time
•  developed by the National Center for Border
Security and Immigration at the University of
Arizona
•  one machine in use in a pilot trial on the US/
Mexico border in Nogales since August 2012
•  uses (at least) voice analysis and body
monitoring
•  not lie-detection proper (yet)
Towards lie-detection using face
recognition: a possible roadmap
- the Facial Action Control System: a system for
analyzing facial expressions (and through them
e.g. emotions) on the hardware level
- 46 Action Units, each representing an individual
facial muscle as seen on the surface
- can also be used to analyze microexpressions,
automatic very short (10...100 ms) expressions
reflecting one’s true mental state before a
conscious concealing expression is displayed,
hence lie detection
- very slow when done manually (100x and up)
Best known from...
Lie to Me: The lead character Cal Lightman is
sort of modelled after Paul Ekman
People are *not* good at this
... seriously.
So what about the law?
- Article 15 of the EU Data Protection
Directive prohibits automated decisions...
- except when authorized by statute or
contract
- adequate safeguards required
- Article 20 of the proposed Data Protection
Regulation approximately similar
- Article 1 of the Regulation defines biometrics
as unique identifiers only
Could AVATAR be used in, say,
Finland?
- yes, authorized by the same statutory
provisions as the current self-service
passport inspection kiosks at certain
Schengen outer borders
- Border Guard Act (578/2005):
29 § on automatical
identification and
31 § on technical
monitoring
Not just Big Government
- widespread use expected in the private
sector as well, e.g.
- advertising
- ...
Regulation of automated decisions
still based on a 1960s mindset
- modern algorithms and technologies beyond
human comprehension (as a whole, at once)
- human supervision and possibility to override
not sufficient alone (general tendency to rely
on machines uncritically)
- such systems should always be required to be
able to output grounds for the decision in a
human-compatible format (at least on
demand)
DIY countermeasures
Anti-face-recognition styling
....and Botox®
Regulation of the legal
profession and legal tech
Law as a regulated profession
-  certain aspects of legal counsel restricted to
persons with a law degree and/or additional
qualifications (bar exam or similar)
-  ownership and management of law firms
restricted to such persons (no outside
investment)
-  restrictions on offering services other than legal
from the same company
-  restrictions on advertising etc.
-  (of course details vary a lot across jurisdictions)
Alternative business structures (ABS)
-  first introduced in England and Wales in 2007
(into effect in 2011), now also at least in
Australia and Canada
-  a firm where a non-lawyer (or a company of
which at least 10% controlled by non-lawyers):
-  is a manager of the firm, or
-  has an ownership-type interest in the firm
-  authorization of ABSs in England and Wales by
the Solicitors Regulation Authority
Meanwhile in Finland...
(against the tide as usual)
-  traditionally very liberal rules regarding
representation in court
-  since 2011: must be either a member of the
bar, a certified representative (oikeuden-
käyntiavustaja) with a law degree, or a close
relative
-  the Finnish bar association’s brand-new
strategy sets representation exclusively by
members of the bar as a goal
-  and maybe after that we get to...
Unauthorized practice of law cases
from the US
-  LegalZoom is probably the company that has
fought this in court the most
-  LegalZoom has outside investors so it can’t be
considered a law firm
-  offering document templates etc. not considered
legal counsel
-  for individualized legal advice LegalZoom can only
operate as a referral service (intermediary between
clients and lawyers)
-  whether an intelligent legal tech system could offer
legal counsel (and hence be prohibited) is still an
open question
Licensed legal technicians
-  introduced in the state of Washington in 2012,
under consideration in NY and CA
-  nothing very techy about it, just a way to allow
paralegals to practice law within a defined
practice area (e.g. family law)
-  regulated by the WA state bar
-  could be a solution for companies like
LegalZoom
-  and maybe offer a roadmap for regulating legal
tech where needed
Intellectual property issues
wrt artificial intelligence
IP issues raised by AI
-  rights to works created by computers
-  use of third-party works (and rights thereto)
in works created by computers
-  (and all the usual software patent nonsense)
Computers as authors
-  check your local listings (e.g. UK yes, Finland
no)
-  compare to animals as authors
Example on third-party works:
Machine translation
-  a wonderfully complex example for showing
that what comes out of the computer has
actually been produced by humans as authors
and translators
-  and how those people may or may not be
copyright holders
-  boils down to very fundamental questions:
-  who can be an author
-  what constitutes a copyright-protected work
(what ways can you use a work so that original
copyrights are exhausted, cf. INFOPAQ)
Machine translation:
The first generation (rule-based)
-  based on an explicit model of the languages
translated to/from
-  first successful experiment in 1954 using a 250-
word vocabulary
-  different methods: direct, syntax-tree analysis,
first-order predicate calculus, interlinguas
-  everything affecting translation really has to be
coded manually in the system one way or the
other ⇒ knowledge-acquisition bottleneck
-  also limited by computer performance (1950s
experiments slower than human translators)
Machine translation:
The first generation (rule-based)
-  first used succesfully for specific domains of
language with limited vocabulary and highly
standardized syntax (eg. weather reports)
-  some high-quality rule-based systems
currently still in use and under further
development, eg. MOT Translation by
Kielikone (en/fi) and GramTrans by
GrammarSoft, Kaldera Språkteknologi and
SDU (da/en/eo/no/kl/pt/es/sv)
Machine translation:
The second generation (statistical)
-  statistical methods first used for speech-to-text in the
1980s, the idea finds its way to the MT community
towards the end of the 1990s
-  enabled by faster computers, cheaper storage, and
the availability of documents in digital formats
-  no longer based on models explicitly specifying every
aspect of the translation
-  instead based on massive collections of multilingual
documents with all the language elements
(sentences, phrases, words, morphemes...) aligned
across each language pair (mostly automatically)
Machine translation:
The second generation (statistical)
-  nowadays the dominant method, used by
eg. Google Translate, Yahoo! BabelFish,
Bing Translator
-  also specialized users, eg. on-line patent
translations at the EPO
-  still far from perfect but usually good
enough for understanding foreign texts
-  works better when combined with rule-
based methods
Why is this an Intellectual Property
issue?
-  the first-generation systems did not use 3rd-party
IP (except maybe for a dictionary or two)
-  the second-generation systems make massive use
of 3rd-party IP through documents available in
both the source and target languages (aligned)
-  machine translation parallel corpora often based
on documents free from copyright (eg. acquis
communautaire)
-  translation memories for computer-aided
translation also used as a commodity shared
between translators and translation agencies
Flow of authorship in
machine translation
Flow of authorship in
Computer-Aided Translation
Questions?

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Introduction to Legal Technology, lecture 8 (2015)

  • 1. TLS0070 Introduction to Legal Technology Lecture 8 Regulatory issues University of Turku Law School 2015-02-24 Anna Ronkainen @ronkaine anna.ronkainen@onomatics.com
  • 2. But first... Final paper -  2500–4000 words (10–16 pages), more does not necessarily imply better! -  to be returned on Moodle by Fri Apr 10, use pdf as file format -  will do my best to grade the papers within 2 weeks after the deadline -  normal academic style, use references (in- text, footnotes, endnotes – whatever you are used to but just be consistent) to document the sources you have used in your work
  • 3. Topic and form -  must be approved by the lecturer in advance (before/after lectures or via e-mail) by end of March at the latest -  possible topics (non-exhaustive list): -  some specific technology and its application to law (in general or to a specific field) -  some specific field of law or type/stage of legal practice and the current/potential application of technology in it (in general or specific) -  a specific legal/regulatory issue related to the use of technology by lawyers or for things normally done by lawyers -  thorough analysis of 1–2 existing legal startup(s) -  business plan for your own future legal startup
  • 4. Specifically for the startup-type topics -  include at least the following -  basic facts about the organization: age, legal structure, geography, founders, funding etc. -  what legal problem they are trying to solve -  competition (including less techy alternatives) -  business model (NB: non-profits are okay too!) -  description of the technologies used -  this is not business school so I don’t expect you to be an expert in that – look at it from a lawyer’s perspective -  commercial viability is a part of the grading criteria if you write about your own imaginary startup -  startup people generally love talking about their work so feel free to try to contact them (but only one paper allowed per startup (and none about TrademarkNow))
  • 6. What is cloud computing? -  remote servers and networks allowing for centralized data storage and online access to services -  SaaS: software as a service -  PaaS: platform as a service -  IaaS: infrastructure as a service -  it’s not exactly new: pre-PC mainframes (including Westlaw/LexisNexis) were also “in the cloud”
  • 7. What’s wrong with it -  it’s new and different -  trust issues wrt cloud service provider -  data protection complications with non-EU cloud providers as data processors -  requires good net connectivity -  risk of eavesdropping
  • 8. On the other side -  cheaper/easier to manage (economies of scale) -  same data and services available across different platforms -  many types of modern technology only available on the cloud -  usually you can get an EU provider if that’s important from a data protection perspective -  usually the weakest link in terms of security is somewhere between the keyboard and the chair
  • 9. Data protection, big data, and automated decisions
  • 10. An example: behavioural biometrics What are biometrics? - the measurement of anatomical, physiological, chemical or behavioural characteristics of an individual - according to the traditional definition only used for the purposes of recognition, verification or identification of a given individual human being (probably mostly because that’s all what the technology could be used for earlier) - e.g fingerprints, iris scans, face recognition, voice patterns, gait patterns, keystroke timing...
  • 11. Biometrics in law - a well-established feature of data protection law for several decades already - special because based on (mostly) immutable characteristics of a given individual: you can’t swap your irises the way you’d change a password -  but that’s just the beginning...
  • 12. Dynamic behavioural biometrics (or biomarkers) (for lack of a better term) - behavioural biometrics: based on acquired individual traits rather than innate features - dynamic: gathered over a longer time interval (usually at least minutes and in a specific context) - can be used for much more than just identification with modern technology - the measurements are not (only) used to compute an identification key but contain other types of (even sensitive) personal data as well
  • 13. State of the art? A simple example: heart rate monitoring from to...
  • 15. On to mind-reading - considerable interest in the use of neuroscience as legal evidence - including functional neuroimaging: a behavioural biometric - the use of fMRI lie detection not yet allowed in the US (attempts at least in Tennessee, New York, Maryland), EEG-based ”lie detection” allowed in Sharma case in Pune, India in 2008 (despite massive criticism by neuroscientists) http://www.nytimes.com/2008/09/15/world/asia/15iht-15brainscan.16148673.html
  • 18. EEG: a more discreet (and crackable) example (emotiv.com)
  • 19. But what about this?
  • 20. AVATAR: Automated Virtual Agent for Truth Assessment in Real-Time •  developed by the National Center for Border Security and Immigration at the University of Arizona •  one machine in use in a pilot trial on the US/ Mexico border in Nogales since August 2012 •  uses (at least) voice analysis and body monitoring •  not lie-detection proper (yet)
  • 21. Towards lie-detection using face recognition: a possible roadmap - the Facial Action Control System: a system for analyzing facial expressions (and through them e.g. emotions) on the hardware level - 46 Action Units, each representing an individual facial muscle as seen on the surface - can also be used to analyze microexpressions, automatic very short (10...100 ms) expressions reflecting one’s true mental state before a conscious concealing expression is displayed, hence lie detection - very slow when done manually (100x and up)
  • 22. Best known from... Lie to Me: The lead character Cal Lightman is sort of modelled after Paul Ekman
  • 23. People are *not* good at this
  • 25. So what about the law? - Article 15 of the EU Data Protection Directive prohibits automated decisions... - except when authorized by statute or contract - adequate safeguards required - Article 20 of the proposed Data Protection Regulation approximately similar - Article 1 of the Regulation defines biometrics as unique identifiers only
  • 26. Could AVATAR be used in, say, Finland? - yes, authorized by the same statutory provisions as the current self-service passport inspection kiosks at certain Schengen outer borders - Border Guard Act (578/2005): 29 § on automatical identification and 31 § on technical monitoring
  • 27. Not just Big Government - widespread use expected in the private sector as well, e.g. - advertising - ...
  • 28. Regulation of automated decisions still based on a 1960s mindset - modern algorithms and technologies beyond human comprehension (as a whole, at once) - human supervision and possibility to override not sufficient alone (general tendency to rely on machines uncritically) - such systems should always be required to be able to output grounds for the decision in a human-compatible format (at least on demand)
  • 30. Regulation of the legal profession and legal tech
  • 31. Law as a regulated profession -  certain aspects of legal counsel restricted to persons with a law degree and/or additional qualifications (bar exam or similar) -  ownership and management of law firms restricted to such persons (no outside investment) -  restrictions on offering services other than legal from the same company -  restrictions on advertising etc. -  (of course details vary a lot across jurisdictions)
  • 32. Alternative business structures (ABS) -  first introduced in England and Wales in 2007 (into effect in 2011), now also at least in Australia and Canada -  a firm where a non-lawyer (or a company of which at least 10% controlled by non-lawyers): -  is a manager of the firm, or -  has an ownership-type interest in the firm -  authorization of ABSs in England and Wales by the Solicitors Regulation Authority
  • 33. Meanwhile in Finland... (against the tide as usual) -  traditionally very liberal rules regarding representation in court -  since 2011: must be either a member of the bar, a certified representative (oikeuden- käyntiavustaja) with a law degree, or a close relative -  the Finnish bar association’s brand-new strategy sets representation exclusively by members of the bar as a goal -  and maybe after that we get to...
  • 34. Unauthorized practice of law cases from the US -  LegalZoom is probably the company that has fought this in court the most -  LegalZoom has outside investors so it can’t be considered a law firm -  offering document templates etc. not considered legal counsel -  for individualized legal advice LegalZoom can only operate as a referral service (intermediary between clients and lawyers) -  whether an intelligent legal tech system could offer legal counsel (and hence be prohibited) is still an open question
  • 35. Licensed legal technicians -  introduced in the state of Washington in 2012, under consideration in NY and CA -  nothing very techy about it, just a way to allow paralegals to practice law within a defined practice area (e.g. family law) -  regulated by the WA state bar -  could be a solution for companies like LegalZoom -  and maybe offer a roadmap for regulating legal tech where needed
  • 36. Intellectual property issues wrt artificial intelligence
  • 37. IP issues raised by AI -  rights to works created by computers -  use of third-party works (and rights thereto) in works created by computers -  (and all the usual software patent nonsense)
  • 38. Computers as authors -  check your local listings (e.g. UK yes, Finland no) -  compare to animals as authors
  • 39. Example on third-party works: Machine translation -  a wonderfully complex example for showing that what comes out of the computer has actually been produced by humans as authors and translators -  and how those people may or may not be copyright holders -  boils down to very fundamental questions: -  who can be an author -  what constitutes a copyright-protected work (what ways can you use a work so that original copyrights are exhausted, cf. INFOPAQ)
  • 40. Machine translation: The first generation (rule-based) -  based on an explicit model of the languages translated to/from -  first successful experiment in 1954 using a 250- word vocabulary -  different methods: direct, syntax-tree analysis, first-order predicate calculus, interlinguas -  everything affecting translation really has to be coded manually in the system one way or the other ⇒ knowledge-acquisition bottleneck -  also limited by computer performance (1950s experiments slower than human translators)
  • 41. Machine translation: The first generation (rule-based) -  first used succesfully for specific domains of language with limited vocabulary and highly standardized syntax (eg. weather reports) -  some high-quality rule-based systems currently still in use and under further development, eg. MOT Translation by Kielikone (en/fi) and GramTrans by GrammarSoft, Kaldera Språkteknologi and SDU (da/en/eo/no/kl/pt/es/sv)
  • 42. Machine translation: The second generation (statistical) -  statistical methods first used for speech-to-text in the 1980s, the idea finds its way to the MT community towards the end of the 1990s -  enabled by faster computers, cheaper storage, and the availability of documents in digital formats -  no longer based on models explicitly specifying every aspect of the translation -  instead based on massive collections of multilingual documents with all the language elements (sentences, phrases, words, morphemes...) aligned across each language pair (mostly automatically)
  • 43. Machine translation: The second generation (statistical) -  nowadays the dominant method, used by eg. Google Translate, Yahoo! BabelFish, Bing Translator -  also specialized users, eg. on-line patent translations at the EPO -  still far from perfect but usually good enough for understanding foreign texts -  works better when combined with rule- based methods
  • 44. Why is this an Intellectual Property issue? -  the first-generation systems did not use 3rd-party IP (except maybe for a dictionary or two) -  the second-generation systems make massive use of 3rd-party IP through documents available in both the source and target languages (aligned) -  machine translation parallel corpora often based on documents free from copyright (eg. acquis communautaire) -  translation memories for computer-aided translation also used as a commodity shared between translators and translation agencies
  • 45. Flow of authorship in machine translation
  • 46. Flow of authorship in Computer-Aided Translation