Discussion paper: How much of commonsense and legal reasoning is formalizable? A review of conceptual obstacles

Law, Probability and Risk

Volumn 11, Issue 2-3, 2012, Pages 225-245

  Franklin, James ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84866425823     PISSN: 14708396     EISSN: 1470840X     Source Type: Journal    
DOI: 10.1093/lpr/mgs007     Document Type: Review

References (94)

1. Pamela McCorduck, MachinesWho Think (2nd ed, Natick, MA:A.K. Peters, 2004
2. MarvinMinsky, Steps towards artificial intelligence, Proceedings of the Institute of Radio Engineers 49 (1961), 8-30.
3. Donald A. Waterman, Jody Paul and Mark Peterson, Expert systems for legal decision making, Expert Systems 3 (1986), 212-26
4. Causal reasoning in law is especially reliant on commonsense methods, as described below.
5. George J. Klir and Tina A. Folger, Fuzzy Sets, Uncertainty and Information (Englewood Cliffs, NJ: Prentice Hall, 1988
6. Jacky Legrand, Some guidelines for fuzzy sets application in legal reasoning, Artificial Intelligence and Law 7 (1999), 235-57.
7. Review of methods in Taner Bilgic and I. Burhan Türksen, Measurement ofmembership functions: theoretical and empirical work, in D. Dubois and H. Prade, eds, Fundamentals of Fuzzy Sets (Norwell, Mass: Kluwer, 2000), ch. 3; introduction with a social science emphasis in Jay Verkuilen, Assigning membership in a fuzzy set analysis, SociologicalMethods and Research 33 (2005), 462-96
8. The methods are complex and consistent results are hard to obtain. A rare, reasonably successful attempt to extract membership functions, but illustrative of the difficulties, in Thomas S.Wallsten, David V. Budescu, Amnon Rapoport, Rami Zwick, and Barbara Forsyth, Measuring the vague meanings of probability terms, Journal of Experimental Psychology: General 115 (1986), 348-65
9. The difficulties of extracting a membership function for 'beyond reasonable doubt' shown experimentally in Mandeep K. Dhami, On measuring quantitative interpretations of reasonable doubt, Journal of Experimental Psychology: Applied 14 (2008), 353-63.
10. Satoro Fukami, MasaharuMizumoto and Kokichi Tanaka, Some considerations on fuzzy conditional inference, Fuzzy Sets and Systems 4 (1980), 243-273
11. Available formalisms for fuzzy implications described in Michal Baczynski and Balasubramaniam Jayaram, Fuzzy Implications (Berlin: Springer, 2008), but without much attention to agreement with natural language.
12. Karen S. Ebeling and Susan A. Gelman, Children's use of context in interpreting 'big' and 'little', Child Development 65 (1994), 1178-92.
13. Herbert L.A. Hart, The Concept of Law (Oxford: Clarendon, 1961), 120-4
14. see Avishai Margalit, Open texture, in A. Margalit, ed., Meaning and Use (Dordrecht: Reidel, 1979), 141-52
15. Brian Bix, H.L.A. Hart and the 'open texture' of legal language, Law and Philosophy 10 (1991), 51-72.
16. Popov v. Hayashi, 2002 WL 31833731 (Ca. Sup. Ct. 2002); legal commentary in Paul Finkelman, Fugitive baseballs and abandoned property: who owns the homerun ball? Cardozo Law Review 23 (2002), 1609-33
17. Patrick Stoklas, Popov v. Hayashi, a modern-day Pierson v. Post: a comment on what the court should have donewith the seventy-third home run baseball hit by Barry Bonds, Loyola University Chicago Law Journal 34 (2002 901-43
18. Michael Pastrick, When a day at the ballpark turns a 'can of corn' into a can of worms: Popov v. Hayashi, Buffalo Law Review 51 (2003), 905-35.
19. T.J.M. Bench-Capon, Representing Popov v Hayashi with dimensions and factors, Artificial Intelligence and Law, 20 (2012), 15-35
20. Douglas Walton, Similarity, precedent and argument from analogy, Artificial Intelligence and Law 18 (2010), 217-46.
21. Marcello Guarini, A defense of non-deductive reconstructions of analogical arguments, Informal Logic 24 (2004), 153-68
22. Federico Picinali, Structuring inferential reasoning in criminal fact finding: an analogical theory, Law, Probability and Risk, this issue.
23. Douglas Lenat, The dimensions of context space, www.cyc.com/context-space.doc
24. Samuel W.K. Chan and James Franklin, Dynamic context generation for natural language understanding: a multifaceted knowledge approach, IEEE Transactions on Systems A: Systems and Humans 33 (2003), 23-41.
25. James Franklin, The representation of context: ideas from artificial intelligence, Law, Probability and Risk 2 (2003), 191-9.
26. Alan Patrick Herbert, More Misleading Cases (London: Methuen, 1930), ch. X.
27. John S. James Stroud's Judicial Dictionary of Words and Phrases (4th ed, London: Sweet and Maxwell), articles 'Animal', 'Domestic animal', 'Ferae naturae1972
28. One of the leaders of AI writes: 'Almost all previous discussion of context has been in connection with natural language. .However, I believe the main AI uses of formalized context will not be in connection with communication but in connection with reasoning about the effects of actions directed to achieving goals. It's just that natural language examples come to mind more readily.' (John McCarthy, Artificial intelligence, logic and formalizing common sense, in R.H. Thomason, ed., Philosophical Logic and Artificial Intelligence (Dordrecht: Kluwer, 1989), 161-90
29. Yehoshua Bar-Hillel, The present status of automatic translation of languages, in Advances in Computers, vol. 1, ed. F.L. Alt (New York: Academic Press, 1960
30. Lee Gomes, Google Translate tangles with computer learning, Forbes Magazine, Aug 9, 2010.
31. Ben-Ami Sharfstein, The Dilemma of Context (New York: NYU Press, 1989).
32. Introduction in Varol Akman and Mehmet Surav, Steps toward formalizing context, AI Magazine 17 (3) (Fall, 1996), 55-72
33. Brezillon, Context in artificial intelligence, Computers and Artificial Intelligence 18 (1999), 321-40.
34. Paolo Bouquet, ChiaraGhidini, FaustoGiunchiglia and Enrico Blanzieri, Theories and uses of context in knowledge representation and reasoning, Journal of Pragmatics 35 (2003), 455-84http://www.eecs.umich.edu/rthomaso/bibs/context.html
35. Douglas Lenat and R.V.Guha, Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project (Reading, Mass: Addison-Wesley, 1990); a recent report in Douglas Lenat et al, Harnessing Cyc to answer clinical researchers' ad hoc queries, AI Magazine 31 (3) 2010 13-32.
36. Lenat'swork here thus connects with extensive research in cognitive psychology under the names of mental models (Philip N. Johnson-Laird, Mental Models: Towards a cognitive science of language, inference and consciousness, Cambridge: Cambridge University Press, 1983) and bounded rationality (Gerd Gigerenzer and Reinhard Selten, eds, Bounded Rationality: The adaptive toolbox (Cambridge, Mass: MIT Press) according to which most normal human inference does not follow the plan of symbolic logic and traditional AI of shuffling symbols by rules, but involves context-specific and relatively simple mental models.2001
37. In A.P. Herbert's only slightly exaggerated caricature: 'No matter what may be the particular department of human life which falls to be considered in these Courts, sooner or later we have to face the question:Was this or was it not the conduct of a reasonable man? Did the defendant take such care to avoid shooting the plaintiff in the stomach as might reasonably be expected of a reasonable man? (Moocat v. Radley (1883) 2 Q.B.) Did the plaintiff take such precautions to inform himself of the circumstances as any reasonable man would expect of an ordinary person having the ordinary knowledge of an ordinary person of the habits of wild bulls when goaded with garden-forks and the persistent agitation of red flags? (Williams v. Dogbody (1841) 2 A.C.) I need not multiply examples. Alan Patrick Herbert, Uncommon Law (London: Methuen, 2.1935
38. AlisonGopnik, AndrewN. Meltzoff and PatriciaK. Kuhl, The Scientist in the Crib:Minds, Brains andHow Children Learn (New York: HarperCollins, 1999
39. Hubert Dreyfus, What Computers Still Can'tDo: ACritique of ArtificialReason (Cambridge, Mass:MITPress, 1992
40. Stevan Harnad, The symbol grounding problem, Physica 42 (1990), 223-46
41. Mariarosaria Taddeo and Luciano Floridi, Solving the symbol grounding problem: a critical review of 15 years of research, Journal of Experimental and Theoretical Artificial Intelligence 17 (2005), 419-45
42. F. Adams and K. Aizawa, Causal theories of mental content, Stanford Encyclopedia of Philosophy 2010, http://plato.stanford.edu/entries/content-causal/
43. Conceptual overview in Antony Honore, Causation in the law, Stanford Encyclopedia of Philosophy, 2001, revised 2010 http://plato.stanford.edu/entries/causation-law/
44. Peter Menzies, Counterfactual theories of causation, Stanford Encyclopedia of Philosophy, 2001, revised 2008 (http://plato.stanford.edu/entries/causation-counterfactual/); AI perspectives in Jos Lehmann, Joost Breuker and Bob Brouwer, Causation in AI and law, Artificial Intelligence and Law 12 (2004), 279-315
45. Rinke Hoekstra and Joost Breuker, Commonsense causal explanation in a legal domain, Artificial Intelligence and Law 15 (2007), 281-99.
46. Anderson v. Minneapolis, St: P. and S. St. R.R. Co., 146 Minn. 430, 179 N.W. 45 (1920), discussed in Fleming James and Roger F. Perry, Legal 31 David Lewis, Counterfactuals (Oxford: Blackwell, 1973), 8-9.
47. David Lewis, Counterfactuals (Oxford: Blackwell, 1973), 8-9.
48. Joshua Knobe and Ben Fraser, Causal judgment and moral judgment: two experiments, in W. Sinnott-Armstrong, ed., Moral Psychology, The Cognitive Science of Morality David Lewis, Counterfactuals (Oxford: Blackwell, 1973), 8-9., 441-82
49. Hitchcock and Joshua Knobe, Cause and norm, Journal of Philosophy 106 (2009), 587-612.
50. Something on the complexity of howhumans do it inDanielKahneman andDale T. Miller, Normtheory: comparing reality to its alternatives, Psychological Review 93 (1986), 136-53.
51. E.G. Coppel, The judicial development of the law, Medico-Legal Society Proceedings (Medico-Legal Society of Victoria), 1938, (http://mlsv.org.au/files/1937-1938/The%20Judicial%20Development%20of%20The%20Law.pdf
52. Richard H. Pildes, Avoiding balancing: the role of exclusionary reasons in constitutional law, Hastings Law Journal 45 (1993
53. Pildes himself prefers a 'ratio decidendi' approach); explicit references to terror cases in Michel Rosenfeld, Judicial balancing in times of stress: comparing the American, British and Israeli approaches to the War on Terror, Cardozo Law Review 27 (2005 2079-150
54. A judicial perspective on the psychology of balancing in FrankM. Coffin, Judicial balancing: the Protean scales of justice, New York University Law Review 16 (1988), 16-42
55. Warnings on cultural differences in how balancing is done in Jacco Bomhoff, Balancing, the global and the local: judicial balancing as a problematic topic in comparative (constitutional) law, Hastings International and Comparative Law Review 31 (2008), 555-86.
56. Joris Naiman, Judicial balancing of uses for public property: the paramount public use doctrine, Boston College Environmental Affairs Law Review 17 (1989893-920.
57. Federico Cheever, Butterflies, cave spiders, milk-vetch, bunchgrass, sedges, lilies, checker-mallows and why the prohibition against judicial balancing of harm under the Endangered Species Act is a good idea, William and Mary Environmental Policy Law and Policy Review 22 (61-811.
58. Peter Tillers, The structure and logic of proof in trials, Law, Probability and Risk 10 (2011), 1-6
59. The classic text isMichael Polanyi, The Tacit Dimension (London: Routledge, 1966
60. Oliver Wendell Holmes, The path of the law, Harvard Law Review 10 (1897), 457-78.
61. James Franklin, The objective Bayesian conceptualisation of proof and reference class problems, Sydney Law Review 33 (2011), 545-61.
62. The mathematician George Polya called this the 'fundamental inductive pattern': G. Polya, Patterns of Plausible Inference (Princeton: Princeton University Press, 1968), 3-4.
63. Leading to some classic problems about whether priors can be derived from symmetry arguments; Bayesian replies in Edwin T. Jaynes, Probability Theory: The Logic of Science (Cambridge: Cambridge University Press, 2003), ch. 15 and James Franklin, Resurrecting logical probability, Erkenntnis 55 (2001), 277-305
64. However, absolute priors are rarely relevant in legal reasoning, where the problem is more the richness of background information than the lack of it.
65. James Franklin, The Science of Conjecture: Evidence and Probability before Pascal (Baltimore: Johns Hopkins University Press, 2001), 76-9.
66. E.g. the reliance on symmetry in the absence of evidence in T.N.T. Management v. Brooks (1979) 23 A.L.R. 345, discussed in Richard Eggleston, Evidence, Proof and Probability (2nd ed, London: Weidenfeld and Nicolson, 1983), 184.
67. Barbara Davidson and Robert Pargetter, Guilt beyond reasonable doubt, Australasian Journal of Philosophy, 65 (1987), 182-97
68. James Franklin, Case comment: United States v. Copeland, 369 F. Supp. 2d 275 (E.D.N.Y. 2005): quantification of the 'proof beyond reasonable doubt' standard, Law, Probability and Risk 5 (2006), 159-65.
69. 895 F. Supp. 460 (E.D.N.Y. 1995), discussed in Peter Tillers, If wishes were horses: discursive comments on attempts to prevent individuals from being unfairly burdened by their reference classes, Law, Probability and Risk 4 (2005), 33-49.
70. David A. Schum, The Evidential Foundations of Probabilistic Reasoning (New York: Wiley, 1994), 81-3.
71. See articles in International J. of Evidence and Proof, special issue on the reference class problem, 11 (2007) issue 1.
72. John Venn, The Logic of Chance (London: Macmillan, 1866), 176.
73. Hans Reichenbach, The Theory of Probability (Berkeley and Los Angeles: University of California Press, 1949), 374.
74. Henry E. Kyburg, The reference class, Philosophy of Science 50 (1983), 374-97
75. Alan Hajek, The reference class problem is your problem too, Synthese 156 (2007), 563-85
76. Mark Colyvan, Helen M. Regan and Scott Ferson, Is it a crime to belong to a reference class?, Journal of Political Philosophy 9 (2001), 168-81.
77. Mark Colyvan and Helen M. Regan, Legal decisions and the reference class problem, International Journal of Evidence and Proof 11 (2007), 274-85
78. Raymond Reiter and Giovanni Criscuolo, On interacting defaults, Proceedings of the 7th International Joint Conference on Artificial Intelligence, (1981), 270-6
79. Pei Wang, Reference classes and multiple inheritances, International J. of Uncertainty, Fuzziness and Knowledge-Based Systems 3 (1995), 79-91.
80. James Franklin, Feature selection methods for solving the reference class problem: Comment on Edward K. Cheng, 'A practical solution to the reference class problem', Columbia Law Review Sidebar 110 (2010) 12-23.
81. James Franklin, What Science Knows: and How It Knows It (New York: Encounter Books, 2009), 118-22.
82. Laurence H. Tribe, Trial by mathematics: precision and ritual in the legal process, Harvard Law Review 84 (1971), 1329-93
83. Peter Tillers, Trial by mathematics - reconsidered, Law, Probability and Risk 10 (2011), 167-73.
84. Works in the 1980s on the nonmonotic logic approach included M. Ginsberg, ed., Readings in Nonmonotonic Reasoning (Los Altos: Morgan Kauffman, 1987
85. The change was marked by such works as Judea Pearl, Probabilistic Reasoning in Intelligent Systems (San Francisco: Morgan Kaufmann, 1988
86. David J. Spiegelhalter, A. Philip Dawid, Steffen L. Lauritzen and Robert G. Cowell, Bayesian analysis in expert systems, Statistical Science 8 (1993), 219-47.
87. Peter Walley, Statistical Reasoning with Imprecise Probabilities (London: Chapman and Hall, 1991).
88. Legal reasons for calculations with the imprecise, with an attempt to use Dempster-Shafer belief functions for the purpose, in KevinM. Clermont, Death of paradox: the killer logic beneath the standards of proof, Cornell Legal Studies Research Paper No. 12-6 (Feb). Available at SSRN: http://ssrn.com/abstract1/419863462012
89. Linda Zagzebski, Recovering understanding, in M. Steup, ed, Knowledge, Truth and Duty: Essays on Epistemic Justification, Responsibility and Virtue (New York: Oxford University Press, 2001), 235-58.
90. Which raises the problemof how the brain does it. Theories include that of Aristotle and the scholastics that the intellect has an immaterial power of grasping universals (Zdzislaw Kuksewicz, The potential and the agent intellect, ch. 29 of The Cambridge History of LaterMedieval Philosophy, ed. N. Kretzmann et al., Cambridge, 1988), and Augustine's theory of divine illumination (Robert Pasnau, Divine illumination, Stanford Encyclopedia of Philosophy, revised 2006http://plato.stanford.edu/entries/illu mination/
91. Henry Prakken, Formalising ordinary legal disputes: a case study, Artificial Intelligence and Law, 16 (2008), 333-59.
92. Note the potential for symbol-grounding problems in the common AI practice of using atomic names like PossessesInGoodFaith which have meaning to humans but not to the computer system.
93. Prakken's views on the 'accrual' or combining of several reasons for the same conclusion in Henry Prakken, A study of accrual of arguments, with applications to evidential reasoning, in Proceedings of the Tenth International Conference on Artificial Intelligence and Law (ICAIL'05), Bologna, 2005, 85-94.
94. This conclusion thus supports the use of diagrammatic methods to organize evidence and legal reasoning, as they allow the discrete structure of propositions and their relations to be displayed in ways easy for humans to grasp. See for example Tim van Gelder, The rationale for Rationale, Law, Probability and Risk 6 (2007), 23-42