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TLS0070 Introduction to
Legal Technology
Lecture 2
Artificial intelligence and
law: the 20th century
University of Turku Law School 2015-01-27
Anna Ronkainen @ronkaine
anna.ronkainen@onomatics.com
Computing prehistory in
general (20th C and before)
Vaucanson automata
-  Jacques Vaucanson (1709–1782), France
-  1737: The Flute Player
-  1738: The Tambourine Player, The Digesting
Duck
-  1745: the first completely automated loom
The Jacquard loom
-  Joseph Marie Jacquard (1752–1834), France
-  developed Vaucanson’s loom further by
making it programmable
-  exchangeable weaving patterns input using
punched cards
Babbage Analytical Engine
-  Charles Babbage (1791–1871), England
-  designed the Difference Engine for
tabulating polynomial functions
-  based on it, designed the Analytical Engine,
a mechanical general-purpose computer
-  none were built at the time
The first programmer
-  Ada Lovelace (1815–1852), England
-  wrote the first programs written specifically
for the Analytical Engine (which did not exist
at the time), generally considered the first
computer algorithms
Algorithm for computing Bernoulli Numbers on the Analytical Engine (page 1), Ada Lovelace, 1843
Hollerith tabulating machines
-  Herman Hollerith (1860–1929), USA
-  generalized the use of punched cards into increasingly
general-purpose mechanical data processing (1886-)
-  founded the Tabulating Machine Company (-> IBM)
-  widespread use of Hollerith machines across many
applications through the 1st half of 1900s
-  downside: Hollerith machines facilitated the Holocaust
(and afterwards gave rise to data protection legislation)
ENIAC and the end of the mechanical
age
-  the first electronic general-purpose
computer
-  built at Penn in 1943–1946, commissioned
by the US Army
-  initial use: calculating artillery trajectory
tables
-  17468 vacuum tubes, 65 m3, 150 kW
-  data input/output with punch cards,
programmed with rewiring
Things start getting smaller:
On to semiconductors
-  transistor developed in 1947 by Bardeen,
Brandain and Shockley (Bell Labs)
-  first transistorized computer built in
Manchester 1953
-  first integrated circuit constructed in 1958:
Jack Kilby (Texas Instruments)
-  first microprocessors in 1971 (Garrett CADC,
TI TMS 1000, Intel 4004)
The beginnings of data
processing in law
The first search-and-replace ever:
s/retarded child/exceptional child/g
-  terminology change in the Pennsylvania health
code in the late 1950s
-  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)
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
Major threads of AI & law
research (non-exhaustive)
Information retrieval (1-st gen)
-  normal database search (exact match or
wildcard characters)
-  Boolean search operators
-  modest practical advances since the 1980s
(with some recent exceptions)
-  legal AI contributions negligible
Administrative automation
-  has been with us since the 1960s (or 1890s if you count
the use of Hollerith machines for the US census...)
-  an absolute must for effective administration on a large
scale
-  works well if the rules to be applied are straightforward
enough (rather hopeless with discretionary rules)
-  seems that implementing new rules in these kinds of
systems is still a major PITA
-  (also an occasional subject of doctrinal work in
administrative law, rule-of-law issues etc., e.g. Kuopus
1988)
Expert systems
-  a big thing in AI in the 1980s
-  basic idea pretty straightforward:
-  you take an expert in some domain (e.g.
some area of law)
-  make them turn their domain expertise into
computable rules
-  add a reasoning engine
-  and voilá, you have a computer giving
expert advice or making expert decisions
Example: British Nationality Act
1-[1] A person born in the United Kingdom after commencement shall be a British
Citizen, if a t the time of birth his father or mother is:
a) a British Citizen, or
b) settled in the United Kingdom
Represented as
Rule 1: X acquires British citizenship on date Y
IF X was born in the UK
AND X was born on date Y
AND X is after or on commencement of the act
AND X has a parent who qualifies under 1.1 on date
Rule 2: X has a parent who qualifies under 1.1 on date Y
IF X has a parent Z
AND Z was a British citizen on date Y
Rule 3: X has a parent who qualifies under 1.1 on date Y
IF X has a parent Z
AND Z was settled in the UK on date Y
Expert systems work (sort of)
-  if the legal rules are straightforward enough:
-  no ambiguity or vagueness regarding the inputs
-  clarity about which rule applies in each situation
-  even in the best case, formalization of rules is far
from trivial (knowledge-acquisition bottleneck)
-  also requires expertise on what to model and what
to leave out (and how to make sure the system isn’t
used beyond its design limits)
-  how much of the expertise really lies in the system
and how much in the user?
-  in a sense, expert systems are doing just fine, it’s
mainly the term that’s fallen into disuse...
Case-based reasoning
-  one possible approach: analyze legal cases in
terms of factors (very common in US
doctrine)
-  use factors to find best match for case at
hand
-  map factors into a network to find
Soft computing: Fuzzy logic and
neutral networks
-  both highly fashionable in AI in the 1980s
-  also some experiments within legal AI in the
early 1990s
-  fuzzy logic was also popular among legal
theorists (mostly on a metaphorical level)
since Reisinger 1972
‘We suggest that fuzzy logic is no more than (over)sophistication of the approximation
approach, that it may give good results in some very special applications, but its
philosophical basis is uncertain generally and very uncertain when applied to open-
textured legal concepts. Both the appearance of precision and the appearance of
generality are spurious.’ (Bench-Capon and Sergot 1985/1988)
Your basic neural network
Ontologies
-  the philosophical meaning of ontology: the
study of the nature of being (what is and
isn’t)
-  in computer science: a way of formalizing
entities in an universe of discourse (concepts
and their relationships etc.)
-  the Semantic Web (Berners-Lee et al 2001)
-  Cyc 1984– (OpenCyc 2002–)
-  WordNet 1985–
Ontologies contain (in very general
terms)
-  individual entities
-  classes of entities
-  attributes for entities
-  relations between entities
-  function terms
-  restrictions
-  rules
-  axioms
-  events (changes to entities)
Ontologies in law
-  Valente’s functional ontology (1995):
-  norms (normative knowledge)
-  things, events, etc. (world knowledge)
-  obligations (responsibility knowledge)
-  legal remedies (reactive knowledge: penalties,
compensation)
-  rules of legal reasoning (meta-legal knowledge,
e.g. lex specialis)
-  legal powers (creative knowledge)
-  (and several others)
Segment from the E-Courts ontology
(Breuker et al 2002)
E-courts top-level ontology
(Breuker et al 2002)
Use of ontologies
-  always exist in a specific context, built for that
(no Begriffshimmel and no point in aiming for
one)
-  can be generated by hand or by machine
-  two very different ontologies can work just as
well (no Right Answer!)
-  very useful for information retrieval (find similar
things that are called something else)
-  can also be used e.g. for similarity metrics
-  categorization hierarchy also interesting from a
cognitive perspective (basic-level concepts etc.)
Argumentation: Wigmore (1905)
Argumentation frameworks
(Dung 1995)
-  a set of arguments, and attack relations
between pairs of arguments (A attacks B)
-  general semantics for argument trees
-  plus specific rules for finding which attack
relation dominates (in case of conflict)
Pros and cons
-  argument maps can illustrate how things are
made (and sometimes also show that some
valid arguments are actually always ignored)
-  easier as a theoretical than a practical
exercise
-  a lot easier when you already have a
decision and have to find a matching
argument scheme
Questions?
All images PD or CC-BY-2.0 Wikipedia unless otherwise
indicated, see the obvious Wikipedia pages for details

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Introduction to Legal Technology, lecture 2 (2015)

  • 1. TLS0070 Introduction to Legal Technology Lecture 2 Artificial intelligence and law: the 20th century University of Turku Law School 2015-01-27 Anna Ronkainen @ronkaine anna.ronkainen@onomatics.com
  • 2. Computing prehistory in general (20th C and before)
  • 3. Vaucanson automata -  Jacques Vaucanson (1709–1782), France -  1737: The Flute Player -  1738: The Tambourine Player, The Digesting Duck -  1745: the first completely automated loom
  • 4. The Jacquard loom -  Joseph Marie Jacquard (1752–1834), France -  developed Vaucanson’s loom further by making it programmable -  exchangeable weaving patterns input using punched cards
  • 5. Babbage Analytical Engine -  Charles Babbage (1791–1871), England -  designed the Difference Engine for tabulating polynomial functions -  based on it, designed the Analytical Engine, a mechanical general-purpose computer -  none were built at the time
  • 6. The first programmer -  Ada Lovelace (1815–1852), England -  wrote the first programs written specifically for the Analytical Engine (which did not exist at the time), generally considered the first computer algorithms
  • 7. Algorithm for computing Bernoulli Numbers on the Analytical Engine (page 1), Ada Lovelace, 1843
  • 8. Hollerith tabulating machines -  Herman Hollerith (1860–1929), USA -  generalized the use of punched cards into increasingly general-purpose mechanical data processing (1886-) -  founded the Tabulating Machine Company (-> IBM) -  widespread use of Hollerith machines across many applications through the 1st half of 1900s -  downside: Hollerith machines facilitated the Holocaust (and afterwards gave rise to data protection legislation)
  • 9. ENIAC and the end of the mechanical age -  the first electronic general-purpose computer -  built at Penn in 1943–1946, commissioned by the US Army -  initial use: calculating artillery trajectory tables -  17468 vacuum tubes, 65 m3, 150 kW -  data input/output with punch cards, programmed with rewiring
  • 10. Things start getting smaller: On to semiconductors -  transistor developed in 1947 by Bardeen, Brandain and Shockley (Bell Labs) -  first transistorized computer built in Manchester 1953 -  first integrated circuit constructed in 1958: Jack Kilby (Texas Instruments) -  first microprocessors in 1971 (Garrett CADC, TI TMS 1000, Intel 4004)
  • 11. The beginnings of data processing in law
  • 12. The first search-and-replace ever: s/retarded child/exceptional child/g -  terminology change in the Pennsylvania health code in the late 1950s -  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
  • 13. 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
  • 14. 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)
  • 15. 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
  • 16. Major threads of AI & law research (non-exhaustive)
  • 17. Information retrieval (1-st gen) -  normal database search (exact match or wildcard characters) -  Boolean search operators -  modest practical advances since the 1980s (with some recent exceptions) -  legal AI contributions negligible
  • 18. Administrative automation -  has been with us since the 1960s (or 1890s if you count the use of Hollerith machines for the US census...) -  an absolute must for effective administration on a large scale -  works well if the rules to be applied are straightforward enough (rather hopeless with discretionary rules) -  seems that implementing new rules in these kinds of systems is still a major PITA -  (also an occasional subject of doctrinal work in administrative law, rule-of-law issues etc., e.g. Kuopus 1988)
  • 19. Expert systems -  a big thing in AI in the 1980s -  basic idea pretty straightforward: -  you take an expert in some domain (e.g. some area of law) -  make them turn their domain expertise into computable rules -  add a reasoning engine -  and voilá, you have a computer giving expert advice or making expert decisions
  • 20. Example: British Nationality Act 1-[1] A person born in the United Kingdom after commencement shall be a British Citizen, if a t the time of birth his father or mother is: a) a British Citizen, or b) settled in the United Kingdom Represented as Rule 1: X acquires British citizenship on date Y IF X was born in the UK AND X was born on date Y AND X is after or on commencement of the act AND X has a parent who qualifies under 1.1 on date Rule 2: X has a parent who qualifies under 1.1 on date Y IF X has a parent Z AND Z was a British citizen on date Y Rule 3: X has a parent who qualifies under 1.1 on date Y IF X has a parent Z AND Z was settled in the UK on date Y
  • 21. Expert systems work (sort of) -  if the legal rules are straightforward enough: -  no ambiguity or vagueness regarding the inputs -  clarity about which rule applies in each situation -  even in the best case, formalization of rules is far from trivial (knowledge-acquisition bottleneck) -  also requires expertise on what to model and what to leave out (and how to make sure the system isn’t used beyond its design limits) -  how much of the expertise really lies in the system and how much in the user? -  in a sense, expert systems are doing just fine, it’s mainly the term that’s fallen into disuse...
  • 22. Case-based reasoning -  one possible approach: analyze legal cases in terms of factors (very common in US doctrine) -  use factors to find best match for case at hand -  map factors into a network to find
  • 23. Soft computing: Fuzzy logic and neutral networks -  both highly fashionable in AI in the 1980s -  also some experiments within legal AI in the early 1990s -  fuzzy logic was also popular among legal theorists (mostly on a metaphorical level) since Reisinger 1972 ‘We suggest that fuzzy logic is no more than (over)sophistication of the approximation approach, that it may give good results in some very special applications, but its philosophical basis is uncertain generally and very uncertain when applied to open- textured legal concepts. Both the appearance of precision and the appearance of generality are spurious.’ (Bench-Capon and Sergot 1985/1988)
  • 24. Your basic neural network
  • 25. Ontologies -  the philosophical meaning of ontology: the study of the nature of being (what is and isn’t) -  in computer science: a way of formalizing entities in an universe of discourse (concepts and their relationships etc.) -  the Semantic Web (Berners-Lee et al 2001) -  Cyc 1984– (OpenCyc 2002–) -  WordNet 1985–
  • 26. Ontologies contain (in very general terms) -  individual entities -  classes of entities -  attributes for entities -  relations between entities -  function terms -  restrictions -  rules -  axioms -  events (changes to entities)
  • 27. Ontologies in law -  Valente’s functional ontology (1995): -  norms (normative knowledge) -  things, events, etc. (world knowledge) -  obligations (responsibility knowledge) -  legal remedies (reactive knowledge: penalties, compensation) -  rules of legal reasoning (meta-legal knowledge, e.g. lex specialis) -  legal powers (creative knowledge) -  (and several others)
  • 28. Segment from the E-Courts ontology (Breuker et al 2002)
  • 30. Use of ontologies -  always exist in a specific context, built for that (no Begriffshimmel and no point in aiming for one) -  can be generated by hand or by machine -  two very different ontologies can work just as well (no Right Answer!) -  very useful for information retrieval (find similar things that are called something else) -  can also be used e.g. for similarity metrics -  categorization hierarchy also interesting from a cognitive perspective (basic-level concepts etc.)
  • 32. Argumentation frameworks (Dung 1995) -  a set of arguments, and attack relations between pairs of arguments (A attacks B) -  general semantics for argument trees -  plus specific rules for finding which attack relation dominates (in case of conflict)
  • 33. Pros and cons -  argument maps can illustrate how things are made (and sometimes also show that some valid arguments are actually always ignored) -  easier as a theoretical than a practical exercise -  a lot easier when you already have a decision and have to find a matching argument scheme
  • 34. Questions? All images PD or CC-BY-2.0 Wikipedia unless otherwise indicated, see the obvious Wikipedia pages for details