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Modeling meaning and knowledge: legal knowledge

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Guest lecture on modelling legal knowledge held 2016-04-25 at the course Modeling meaning and knowledge at the University of Helsinki, Department of Modern Languages.

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Modeling meaning and knowledge: legal knowledge

  1. 1. Modeling meaning and knowledge: legal knowledge Anna Ronkainen Chief Scientist, TrademarkNow Inc @ronkaine 2016-04-25
  2. 2. My professional background -  studies in EE/CS, law, linguistics, will finish my LL.D. in legal theory eventually (all articles published already) -  worked in language technology development since 1995 -  misc stints in academia, including teaching IP law here and legal tech in U of Turku -  co-founded TrademarkNow (originally Onomatics) in 2012
  3. 3. Law is just a bunch of rules, right? if steal_thing then go_to_jail
  4. 4. Think about buying a cup of coffee... Simple enough, right? -  order -  pay -  drink and leave (not necessarily in that order)
  5. 5. Then think about all the legal issues involved -  (un?)specified amount of liquid with somewhat specified qualities changes owner
  6. 6. Then think about all the legal issues involved -  (un?)specified amount of liquid with somewhat specified qualities changes owner -  what about ownership of the container? -  a non-exclusive lease to use some part of the premises for some amount of time? -  probably a packet of sugar at no extra cost, maybe two, or a kilo? -  plus all the liability issues...
  7. 7. Of course you can also engineer away all the uncertainties...
  8. 8. ...but that kind of limits your options -  conceptual vagueness is an intrinsic part of pretty much any situation worth analyzing in legal terms -  often it is hidden from view thanks to human cognition, which is why legal theory has focused on the most contentious cases -  but it is unescapable in computational modelling even for easy/unproblematic cases
  9. 9. Why?
  10. 10. ”As we know, there are known knowns. There are things we know we know. We also know there are known unknowns, that is to say, we know there are some things we do not know. But there are also unknown unknowns, the ones we don’t know we don’t know.” – Donald Rumsfeld (2002)
  11. 11. (Un)known (un)knowns known unknowns known knowns unknown unknowns ??
  12. 12. (Un)known (un)knowns known unknowns known knowns unknown unknowns unknown knowns
  13. 13. (Un)known (un)knowns conscious ignorance conscious knowledge unconscious ignorance unconscious knowledge
  14. 14. Dual-process cognition System 1 •  evolutionarily old •  unconscious, preconscious •  shared with animals •  implicit knowledge •  automatic •  fast •  parallel •  high capacity •  intuitive •  contextualized •  pragmatic •  associative •  independent of general intelligence System 2 •  evolutionarily recent •  conscious •  distinctively human •  explicit knowledge •  controlled •  slow •  sequential •  low capacity •  reflective •  abstract •  logical •  rule-based •  linked to general intelligence (Frankish & Evans 2009)
  15. 15. Systems 1 and 2 in legal reasoning: interaction System 1: making the decision System 2: validation and justification (Ronkainen 2011)
  16. 16. These are few of my favourite things...
  17. 17. Classical (crisp) logic 0 1 no yes
  18. 18. Fuzzy logic 0 0.5 1 no meh yes
  19. 19. Fuzzy logic 0 0.1 0.5 0.9 1 hell no no meh yes hell yes
  20. 20. Second-order/Type-2 fuzzy logic 0.1±0.1 0.5±0.2 0.9±0.1 no meh yes
  21. 21. Systematizing Estonian laws through self-organization -  project carried out at Tallinn U of Tech by Täks et al -  legal acts modelled as term vectors (based on occurrences of individual words in each document) which are used to generate a self-organizing map (SOM, Kohonen) -  provides a 2-dimensional map of hypothetical (and also actual) relationships between statutes
  22. 22. (Täks & Lohk 2010)
  23. 23. (Täks & Lohk 2010)
  24. 24. 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)
  25. 25. Segment from the E-Courts ontology (Breuker et al 2002)
  26. 26. E-courts top-level ontology (Breuker et al 2002)
  27. 27. 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.)
  28. 28. Modeling meaning and knowledge: legal knowledge Anna Ronkainen Chief Scientist, TrademarkNow Inc @ronkaine 2016-04-25
  29. 29. Questions? Thank you!
  30. 30. A few words about commercializing academic research...
  31. 31. The real innovator’s dilemma 1.  do research 2.  ... 3.  profit!
  32. 32. 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
  33. 33. 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
  34. 34. 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...)
  35. 35. 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)
  36. 36. 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
  37. 37. Questions? Thank you!
  • AndreaJeanelle

    Dec. 15, 2019

Guest lecture on modelling legal knowledge held 2016-04-25 at the course Modeling meaning and knowledge at the University of Helsinki, Department of Modern Languages.

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