14-11-19

AI and Law for theDutch police: beyond
mere prediction.

Floris Bex

What is Artificial Intelligence?

Systems that exhibit intelligent behaviour by analysing
their environment and - with a certain degree of autonomy- taking action to achieve specific objectives.
European Commission
Coordinated strategy on AI

What is Artificial Intelligence?

The AI in question, machine learning, is a technique forrecognising patterns in relevant and preferably ascomplete as possible data files with the aim of discovering
patterns in reality.

Minister of Justice to Parliament of theNetherlandsA brief history of AI

A (very) brief history of AI & Law••••

1977: Law as a computer programme•TAXMAN – executable law•

The British Nationality Act as a Logic Programme•

Case-based reasoning, legal (rule-based) argumentation

1986: Expert system for legal reasoning
1990-2000-now: Formal models of legal reasoning
2005-now: Machine learning•

Legal information retrieval, text classification & summarization, QLP

Quantitative Legal Prediction (QLP)

Quantitative Legal Prediction in the news…

7060

low risk

Age50

Quantitative Legal Prediction

40

high risk30Recidivism

20No recidivism
Unknown

1001

2

345

Prior convictions• Supervised machine learning – finding patterns inpreviously decided “cases”•Case features (date, judges, gender defendant, previous
crimes, number of crimes, avg. income…, text of verdict)•Decision labels (affirm – reverse, high risk - low risk, highcrime – low crime)6789

Predicting SCOTUS decisions•Given “meta-level”
information about cases(justice(s), term, court oforigin, lower court decision,issue area) classify cases aseither AFFIRM or REVERSE

Predicting SCOTUS decisions•

The model correctly classifies 70.2% of
the Court's decisions from between 1816
and 2014•

Outperforms a coin flip (50% accuracy)•On average, outperforms “guess the mostfrequent decision in the 10 years before thecase you’re classifying” (M = 10 baseline, 67.5%accuracy)•Does not outperform M=10 baseline on large
periods before 1860 and after 2006

Katz. Et al.’s algorithm vs M=10 baseline
Green – Katz more accurate
Red – M=10 more accurateA critical look at QLP (1)•Do we have practical uses for predicting the behaviorof SCOTUS?
• SCOTUS-watching - however the model does not perform
well on “novel” decisions• Empirical legal research – however, there are better(more insightful) statistical techniques out there•

Is this really meaningful legal decision making?
• No!

Predicting ECHR verdicts••

Given the text of decisions of theEuropean Court of Human Rights,
classify cases as either VIOLATION
or NON-VIOLATION
Not all section of the decision textin are included• Verdict section section often
explicitly states, e.g., “a violation
was found”

Predicting ECHR verdicts•

Each case is represented as•(combinations of) words (N-grams)••[police], [police officer], [the case],[was caused], [rights], [June],[a Bulgarian], [her claim was]clusters of N-grams (Topics)•Topic 4 - Treatment by state officials
police, officer, treatment, police officer,
July, ill, force, evidence, ill treatment,arrest, allegation, police station,subjected, arrested, brought,
subsequently, allegedly, ten, treated,beatenPredicting ECHR verdicts••Aletras et al.’s model correctly classifies 79% of ECHR
decisions on 580 cases for article 3, 6, 8.
Medvedeva et al.’s model correctly classifies 77% ofECHR decisions on 1942 cases for article 3, 6, 8.
•

Medvedeva does not include ”law” section•

Both outperform “always guess violation” on the dataset (50%accuracy)•

Both would not outperform “always guess violation” on full ECHR
dataset (+43k cases, ±84% violation cases)

Art 13

Violation

0.85

0.83

0.84

Art 14

Non-violation

0.77

0.76

0.77

Art 14Violation

0.77

0.77

0.77

Predicting ECHR verdicts•••

Indicates predictive words and topics
Fig. 3 Coeﬃcients (weights) assigned to diﬀerent n-grams for predicting violations of Article 2 ofECHR. Top 20 ‘violation’ predictors (blue on the right) and top 20 ‘non-violation’ predictions (red onper article
the left). (Color figure online)
Meaningful: “police”,
As we can see from the table, ‘violation’ and ‘non-violation’ are predicted very
“ill treatment”, “thesimilarly for each article.
During the training phase, diﬀerent weights are assigned to the bits of informaChechen republic”
tion it is given (i.e. n-grams) and a hyperplane is created which uses support vectors to maximise the distance between the two classes. After training the model,
But also: “June”,
we may inspect the weights in order to see what information had the most impact“ten”, “the applicant
13had”

A critical look at QLP (2)•Do we have practical uses for predicting the behaviorof ECHR?
• ECHR-watching – but prediction acc. too low• NLP in Empirical legal research – explainability/semanticinterpretability is a problem•

Is this really meaningful legal decision making?
• No! (but Medvedeva and to a lesser extent Aletras don’t
claim it is).

Predicting the outcome of traffic fine appeals•

Appeal to traffic fine goes to court via Public
Prosecutor• Public prosecutor adds opinion before sending it to court•

Predict court decision (justified, not justified, change,not admissible) given the text of the prosecutor• Accuracy around 65% (random guessing 25%, guessing
not justified 45%).

•System also provides similar cases• Based on doc2vec similarity

QLP – what is it good for?

•

Routine cases• Help with triage -> prioritizing cases, online-case-assessor•

However:• Still little connection to the law, “correlation machines”• Explaining decisions at a meaningful level•

Can it help the police?

At the forefront of the developments in AI

AI for the Dutch Police•

Practical uses of AI for the Dutch police• Searching through open source intel, images, etc.
• Automating routine cases•

Accurate, Efficient , Transparent, Controllable,ContestableAI for the Dutch police•

For some types of tasks predictive machine learning is
the best solution• Recognizing guns
• Recognizing online threaths

AI for the Dutch police – beyond prediction•

For other types of tasks, using only machine learning is
not a good idea
• (Autonomously) making (legally relevant) decisions basedon input••

QLP does not make decisions
QLP is not (very) transparent, controllable

AI for the Dutch police – beyond “mere” prediction•Handling citizen crime reports

PoliceemployeeAIcitizens

Online trade fraud•

Online trade fraud• Fraud on eBay, internet forums, fake websites•

40,000 reports filed per year•

Legal background: article 326 of Dutch Criminal Code• Take some good or money away from someone, while“misleading through false contact details, deceptive tricks
or an accumulation of lies”

System for handling citizen reports on onlinetrade fraud••

Submitted online
Given report, decide:• Fraud• No fraud• More information needed à ask questions“was a product delivered?”, “did you wait for theproduct?”, “Did the seller use a false location?”•

Currently done by humans

Why not handle reports using QLP?

•

Given input form (& text), classify “fraud” or “not fraud”• Low precision & recall (F1 ≈ 0.59)• Unclear what the main factors are for the decision•

Given input form (& text), decide on a question• Not enough data, data too noisy (human decision-makers
are not very consistent)

Kos, Schraagen, Brinkhuis, Bex (2017) Classification in a Skewed Online Trade Fraud
Complaint Corpus. 29th Benelux Conference on Artificial Intelligence (BNAIC 2017)

Beyond mere prediction•Extract basic observations from the form/text• waited/not-waited, false-location/noy-false-location•

Use rule-based argumentation to determine fraud/not-fraud• Rules based on legal rules & reasoning••

Ask the right questions based on info that is missing to draw
a conclusion
Determine a strategy for efficiently asking questions• Formal heuristic for short dialogues• Reinforcement learning for efficient strategies

System architecture

Deceptive trick?

False location
Complainant
waited?

PaidFictitious examplereport
I would like to report fraud. I recently saw a bicycle for sale atMarktplaats and contacted advertiser John Doe. Because hesaid that he lived in North Groningen, we agreed that he would
send the bicycle to my home address in Maastricht. I paid him
in good faith but still have not received the bike. Mr. Doe does
not respond to my e-mails any more. I did some research andsawon his Facebook profilethat helives in Roermond.

Not delivered

Information Extraction

Argumentation

Question Policy

Information extraction
Extract observations from free text

False location
PaidNot delivered

Information extraction•Named Entity Recognition• Enriched version of NER module in a well-known Dutch
NLP package (Frog; 92% precision, 82% recall)•Relation extraction using LSTMs (94-99% accuracy)•Classifying texts according to observations• Practical solution, ± 90% accuracy (lower recall)

Schraagen, Brinkhuis, Bex, (2017) Evaluation of Named Entity Recognition inDutch online criminal complaints. Computational Linguistics in theNetherlands Journal, 7.

Schraagen, Bex (2019) Extraction of semantic relations in noisy usergenerated law enforcement data, Proceedings of the 13th IEEE Internation
Conference on Semantic Computing (ICSC 2019).

Argumentation•

Rule-based argumentation consisting of:• Facts observable in text of complaint• Inference rules•

Based on the law and expert knowledge• art 326 Criminal Code, case law, expert police knowledge

Argumentation example•Scenario:
Notdelivered

DeliveryfailureFalse
location

WaitedNot sent

Productpaid

Presumably
fraud

Deception

False
websiteArgumentation example•Scenario:• False location, paid, waited,
not delivered

Notdelivered

DeliveryfailureFalselocation

WaitedNot sent

Productpaid

Presumably
fraud

Deception

Falsewebsite

Argumentation example•

Scenario:•• False location, paid, waited,
not delivered
Conclusion: presumably fraud•Notdelivered

DeliveryfailureFalse
location

WaitedNot sent

Explanation:“This case is presumably fraud, because thecounterparty provided a false location and did notsend the produc”

Productpaid

Presumably
fraud

Deception

Falsewebsite

Exceptions to rules

•

•

•

Scenario:• False location, paid, waited,delivery failure, deception
Cannot conclude presumablyfraud!

Not
delivered

DeliveryfailureFalse
location

WaitedNot sent

Explanation: “For fraud the counterparty should
not have sent the product. In this case, however,there was a delivery failure.”

Productpaid

Presumably
fraud

Deception

Falsewebsite

Dialogue example…. Not delivered …. Paid ….
…. False location …
Did the counterparty use afalse website?
NoHow long have you waited
after he promised you tosend the product?
I’ve waited for three weeks.

Question policy determination•••

Goal: Given observations,find next question(s)Efficiently reaching a conclusion• Only ask relevant questions
Approximation algorithm fordetermining relevant questions

Testerink, Odekerken, Bex (2019) A Method for Efficient Argument-based Inquiry, 13th International
Conference on Flexible Query Answering Systems (FQAS 2019). Lecture Notes in Artificial Intelligence.

Not
delivered

Not sent

False
location

WaitedProductpaid

Presumablyfraud

False
websiteDeception

Question policy learning••

Goal: Given observations,find next question(s)Efficiently reaching a conclusion• Include probabilities of user
responses• Hypothesizing over all possible
future questions?

Notdelivered

Not sent

False
location

WaitedProductpaid

Presumably
fraud

False
websiteDeception

NotdeliveredReinforcement learning forquestion policies••

Learn from userinteraction
Reward function:• Reaching conclusion
à high reward• Each action à small
penaltyFalse
location

WaitedProductpaid

Not sent

False
websiteDeception

Presumably
fraud

80%
Not
delivered

WaitedNot sent

False
websiteFalse
location

Product
paid

Waited?

Deception

Presumably
fraud

20%

Not
delivered

False
location

WaitedNot sentProductpaid

Presumably
fraud

Deception

False
websiteConclusions – AI for handling reports•

Combining symbolic (“old-fashioned”) and sub-symbolic
techniques for legal decision-making•

Drawback of QLP• Not controllable, contestable, transparent, (accurate) -> use
argumentation & more fine-grained subsymbolic machinelearning• System doesn’t “act” -> use argumentation, dialogue•

Drawback of symbolic AI• Interpreting the law -> knowledge acquisition and validation• Handling unstructured data -> use subsymbolic NLPQuestions?

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