modeling meaning and knowledge: legal knowledge

Modeling meaning and knowledge: legal knowledge Anna Ronkainen Chief Scientist, TrademarkNow Inc @ronkaine 2016-04-25

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

Law is just a bunch of rules, right? if steal_thing then go_to_jail

Think about buying a cup of coffee... Simple enough, right? -  order -  pay -  drink and leave (not necessarily in that

order)

Then think about all the legal issues involved -  (un?)specified amount of liquid with

somewhat specified qualities changes owner

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...

Of course you can also engineer away all the uncertainties...

...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

Why?

”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)

(Un)known (un)knowns

knownunknowns

knownknowns

unknownunknowns

??

(Un)known (un)knowns

knownunknowns

knownknowns

unknownunknowns

unknownknowns

(Un)known (un)knowns

consciousignorance

consciousknowledge

unconsciousignorance

unconsciousknowledge

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&Evans2009)

Systems 1 and 2 in legal reasoning: interaction System 1: making the decision System 2: validation and justification

(Ronkainen2011)

These are few of my favourite things...

Classical(crisp)logic

01

noyes

Fuzzylogic

00.51

nomehyes

Fuzzylogic

00.10.50.91

hellnonomehyeshellyes

Second-order/Type-2fuzzylogic

0.1±0.10.5±0.20.9±0.1

nomehyes

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

(Täks&Lohk2010)

(Täks&Lohk2010)

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

(Breukeretal2002)

E-courts top-level ontology

(Breukeretal2002)

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.)

Modeling meaning and knowledge: legal knowledge Anna Ronkainen Chief Scientist, TrademarkNow Inc @ronkaine 2016-04-25

Questions? Thank you!

A few words about commercializing academic research...

The real innovator’s dilemma 1.  do research 2.  ... 3.  profit!

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? ye

s

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

Questions? Thank you!