Justice A. Intelligence

1. Justice A. Intelligence Nikhil Madan Pulkit Sinha Rahul Srinivasan Rushil Goel

2. Is Judgment Day near? Courtesy Google

3. Misconceptions about Law • Law is nothing more than Logic. • Laws are just a set of facts with inference rules that can be used to make judgments • Extracting meaning from legal texts is just a matter of getting through the long, dense passages

4. Challenges in Legal Interpretation • Some terms may have ambiguous meanings – many cases arise mainly on their difference in interpretation • Most legal terms are “Open-Textured” which have indeterminate meanings – do not specify necessary and sufficient conditions for inclusion into a class • Hart’s famous example “Vehicles are not permitted in this park” • Are baby carriages allowed? • Are fire engines prohibited in case of a fire?

5. Logical deduction doesn’t suffice • Legal rules are more heuristic in nature • No ironclad inference laws – rules often have exceptions • In most former British colonies (including India and USA), common law is based on stare decisis – doctrine of precedent • The judgment of similar cases in the past guide present rulings by analogy

6. Adversarial and Fluid nature of Law • Disputes are resolved through argumentation – different interpretations of facts and rules, their relevance and consequences can lead to opposite conclusions • Depending on past cases and changing societal values, legal concepts and rules evolve • Law is inherently non-monotonic in nature :- past results may be limited or overturned • E.g., new DNA evidence might acquit the accused

7. So, how can we use AI in law? • We look at some areas of research • Logical Formalism of Legal Reasoning • Burden of Proof in Legal Argumentation • Applications of AI in Criminal Investigations • Influence of AI on law

8. Uncertainity in Legal Reasoning • Infer new conclusions in the absence of evidence against them • Inherently non-monotonic

9. As an example • The law states that a thief should be punished • What if the thief is mentally ill?

10. Legal norms • Represented as a legal knowledge base. • Set of strict conditionals in classical logic • Set of defeasible conditionals

11. Strict conditionals vs. Defeasible Conditionals • Strict conditionals - Always hold • Defeasible Conditionals - Prima-facie legal norms

12. Transformation • Definition : A transformation is performed iff is brought forward as a reason for , and does not deductively entail . • A defeasible jump to a legal consequence in the absence of reasons against it • A transformation is denoted by

13. Knowledge Base • Represented as (D,L) • L : set of legal norms of the form • D : set of defeasible legal norms of the form

14. Example of a Knowledge Base Rules represent the following situations: a statement (st) is normally not written (¬w), and is normally not punishable (¬p), a defamation (d) is normally a statement and is normally punishable (p), a libel (l) is normally written (w) and is definitely a defamation, a slander (sl) is definitely a defamation.

15. Which interpretation do you pick? • Given a set of defeasible legal norms, we need to establish preference relations between the interpretations that a (legal) agent foresees as possible • We establish this ordering by partitioning the knowledge base and evaluating each interpretation with respect to the partition

16. Tolerance • A conditional with antecedent and consequent is tolerated by a conditional set iff

17. Partitioning the knowledge base • The partition of is said to be n-consistent if it has the following property: Every defeasible legal norm belonging to is tolerated by where n is the number of the subsets in the partition. • No partition of L is done, as a strict legal norm can not be overruled. Only defeasible ones can be overruled.

18. Strength of Legal Norm This induces a natural ordering on the defeasible norms

19. Picking interpretations • Each interpretation is ordered according to the ranking of the defeasible conditional sets it falsifies • The lexicographic entailment prefers the interpretations that falsifies a “lighter” set of defeasible rules, than a model that falsifies a more “serious” one.

20. Criteria to compare the interpretations • the specificity of the falsified defeasible sentences by each interpretation • the size of the set falsified by each interpretation • In situations when these two criteria will be in conflict, a rational agent should prefer a criterion rather than the other. Based on these two rules, an ordering is induced on the set of possible interpretations.

21. Argumentation in Law • An adequate theory of legal reasoning must provide a sound basis • Argumentation serves as a framework for a practical definition of proof and proof procedure • Burden of proof introduces a mechanism for determining the outcome of an argument

22. What is Argumentation? • An argument comprises data supporting or refuting a claim. • Warrant - the connection between data and claim • Every claim has a qualification : valid(!), strong (!-), credible (+), weak(-), and unknown (?) • Any claim is subject to rebuttal arguments • All claims, including input data, must be supported

23. Warrants… • The relationship link between the antecedent and consequent of a warrant can either be explanatory or co-relational • Strength with which its consequent can be drawn from its antecedent • Sufficient, default, and evidential

24. Search Algorithm • Side-1 in support of a claim and Side-2 in support of its negation. • Side-1 attempts support for the input claim. • Given a claim, search for support proceeds from the input claim toward input data • The process has been completed when all claims are supported by propositions in the input • If no initial support can be found, the argument ends with a loss for Side-1.

25. Search Algorithm (Contd.) • Control passes to Side-2, which tries to refute the argument for claims established by Side-1. • Two types of refutation actions – rebutting and undercutting • Defeating Arguments

26. Heuristics • Heuristics • valid reasoning steps are preferred over plausible steps • moves that are defeating are preferred over moves that only make a claim controversial • moves that attack a supporting argument closer to the overall claim are preferred • undercutting moves are preferred over rebutting moves. • Warrants are also ordered according to the following criterion • stronger warrant types are preferred • warrants for which the antecedent currently has no known contradictory support are preferred.

27. Burden of Proof • Burden of Proof • which side of the argument bears the burden • what level of support is required of that side • Defendable argument - one that cannot be defeated with the given warrants and input data

28. Standards of Proof • Scintilla of evidence - at least one weak, defendable argument • Preponderance of the evidence - at least one weak, defendable argument outweigh the other side’s arguments • Dialectical validity - at least one credible, defendable argument defeat all of the other side’s arguments • Beyond a reasonable doubt - at least one strong, defendable argument defeat all of the other side’s arguments • Beyond a doubt - at least one valid, defendable argument defeat all of the other side’s arguments

29. Burden of Proof and Argumentation • Burden of proof plays several roles in the process of argumentation: • as basis for deciding relevance of particular argument moves • as basis for deciding sufficiency of a side’s move • as a basis for declaring an argument over • as a basis for determining the outcome

30. An Example • w1 (loose bricks) --> (maintenance deficiency) • w2 (maintenance deficiency) --> (landlord responsible) • w3 (landlord responsible)) --> (not (tenant responsible)) • w4 ((loose bricks)(near road)) --> (danger) • w5 (danger) --> (tenant responsible) • w6 ((loose bricks)(near road)(seldom used)) --> (not (danger)) • dl (loose bricks) • d2 (near road) • d3 (seldom used) • (claim (landlord responsible)

31. Application to Common Law • Burden of proof – a useful aspect of a computational model of argumentation • Precedent : Prior cases as antecedents, with conclusions representing case outcomes

32. Application of AI in Criminal Investigation • We have looked at applications of AI for understanding legal reasoning and argumentation • But before the cases reach the court, prosecutors need a complete investigation to build a tight case • Two crucial tasks in crime investigation – • Evidence collection • Hypothesis formulation • AI helps human investigators in considering alternative hypothesis simultaneously

33. The approach • Different crime scenarios like homicide vs. suicide can produce similar evidence • Given the evidence collected, and a form of abductive reasoning, a set of possible scenarios which may produce the evidence are constructed • Scenarios are made up of fragments – this increases robustness to handle unforeseen cases • The system suggests new evidence that investigators may want to search for

34. The Framework From [6]

35. The Problem • Crime scenarios describe real world states and events • Evidence, from forensic tests etc. is available • Hypotheses are properties of the crime like possible weapons, perpetrators etc. • We wish to find hypotheses that follow from the scenarios that support all the available evidence

36. An overview of the algorithm • Given a set of reusable scenario components and evidence, the scenario instantiator constructs possible scenarios • These are fed into the ATMS to infer circumstances under which a certain crime scenarios is possible • The ATMS maintains information and assumptions as nodes and inferences as relations between nodes • The results are analyzed by the query handler for answering: • Which hypotheses are supported by evidence? • What additional evidence can strengthen a hypothesis?

37. The inference mechanism • Initialize the ATMS with the evidence that is available • All possible sets of events and states that can produce the given evidence are reconstructed by considering the possible applicable scenario fragments • All evidence and hypotheses that follow from the results of the last phase are generated by consider scenario fragments • The possible inconsistencies that may arise in the above phases are reported

38. An Example From [6]

39. An Example • The case considered herein involves homicidal or accident death of babies due to a subdural hemorrhage. A subdural hemorrhage is a leakage of blood from vessels on the underside of the dura, one of the membranes covering the brain. It is a common cause of death of abused babies (the so-called shaken baby syndrome), but the injury may also be due to a number of non-homicidal causes, such as complications at birth, early childhood illnesses and certain medical procedures.

40. Decision Making • The supported hypothesis are those implied by environments which support the given evidence. • E.g., in above cases, both accident and homicide are hypotheses consistent with the evidence • If we can find circumstances under which both the evidence and hypothesis are supported, the evidence can be found under the given hypothesis • In the above case, reduced collagen synthesis in the medical report may be found if an accident had occurred

41. Summary • If a sound and complete set of crime scenario segments can be developed, the above procedure helps investigators by looking at multiple scenarios simultaneously • Further improvements may involve mechanisms for weighing different possible scenarios to help focus the investigators’ efforts

42. Do we need laws for Robots? • We’ve seen how AI is used to automate and assist the legal process • Robots will eventually become autonomous beings, capable of performing tasks in unstructured environments. • We need laws to regulate the behavior of robots, which gives rise to a number of legal issues.

43. Safety Intelligence • Need to limit robot ‘self-control’ • A system of regulations restricting artificial intelligence • Isaac Asimov’s Three Laws of Robotics: • A robot may not injure a human being, or, through inaction, allow a human being to come to harm • A robot must obey orders given it by human beings, except where such orders would conflict with the First Law • A robot must protect its own existence as long as such protection does not conflict with the First or Second Law • Believed to be a good foundation for constructing actual Laws

44. Safety Intelligence - Challenges • ‘Open Texture’ laws • Legal dilemma about status of robots – will they exist as properties of human beings or as independent beings. • Exceptions and contradictions in Law • E.g. Law enforcement, surgical operation • Making robots with sufficient intelligence to obey the law.

45. Limitations of current Approaches • Problems with Case-based reasoning • Assumes certain characteristics about legal reasoning • Difficult to ascertain the true nature of reasoning employed by lawyers and judges. • Often the principle behind a particular judgment is more important in determining its relevance to particular case than the exact details of the case

46. An Example • An airline dismisses a co-pilot for refusing to fly a plane on the ground that it is unsafe to fly • This may be similar to cases of discharge of employees for refusal to commit perjury, in that both cases the employer’s actions threaten third parties • There may be different principles operating in similar cases e.g. pro-employee vs. pro-employer judgments above • The choice of which precedent to follow cannot be determined based only on the closeness of factors, but rather by the goodness of principles involved

47. Limitations of current Approaches • Data collection and semantic interpretation is often a stumbling block for the implementation of AI systems • Ensuring the knowledge base fed to the AI system is sound and complete is the user’s responsibility • E.g. In the criminal scenario system discussed before, all possible causes of an hemorrhage need to be fed into the system. This process needs extensive knowledge in the particular field on the user’s part

48. Conclusion • There has been significant progress in applying AI to the legal domain • Some problems still need to be tackled for more widespread use of these ideas • Hopefully, future work will lead to a better understanding of the legal process and greater synergy between AI and law • Maybe, we’ll even have Robot Judges!

49. Thank You! Thank You!

50. Bibliography • Cass R. Sunstein, Of Artificial Intelligence and Legal Reasoning (Chicago Public Law and Legal Theory Working Paper No. 18) • Edwina L. Rissland, Artificial Intelligence and Legal Reasoning (AI Magazine Volume 9 Number 3, 1988) • Edwina L. Rissland and Kevin D. Ashley, A note on dimensions and factors (Artificial Intelligence and Law 10: 65-77, 2002) • Arthur M. Farley and Kathleen Freeman, Burden of Proof in Legal Argumentation (ACM, 1995) • Yueh-Husuan, Chien-Hsun Chen and Chuen-Tsai Sun, The Legal Crisis of Next Generation Robots: On Safety Intelligence (ICAIL ’07)