The design of an attorney's statistical consultant

This paper discusses the design of an attorney’s statistical consultant being developed by the Palo Alto, California Office of the American Institutes for Research, a company that, for the past seven years, has provided expert statistical support to both plaintiffs’ and defendants’ counsel in large, Title VII, employment discrimination suits. The paper focuses upon that portion of the attorney’s statistical consultant that assists its users in discovering and evaluating the truth of the critical assumptions and presuppositions involved in statis tical exhibits and in their use as data in legal argument. The discussion begins with some general design considerations and then focuses upon the way in which the system analyses an argument for rebuttal, presenting in turn the data structures and algorithms employed to implement such analysis and the transcript of an interactive session that exemplifies it.

[1]  JOHN F. Young.  Machine Intelligence , 1971, Nature.

[2]  Sandy L. Zabell,et al.  Statistical proof of discrimination , 1989 .

[3]  Edwina L. Rissland,et al.  AI and Legal Reasoning , 1985, IJCAI.

[4]  S. Toulmin.  The uses of argument , 1960 .

[5]  James A. Reggia,et al.  Diagnostic Expert Systems Based on a Set Covering Model , 1983, Int. J. Man Mach. Stud..

[6]  B. Baldwin,et al.  An Essay on Metaphysics , 1941, Nature.

[7]  Norwood Russell Hanson,et al.  Patterns of discovery : an inquiry into the conceptual foundations of science , 1958 .

[8]  Harry E. Pople,et al.  The Formation of Composite Hypotheses in Diagnostic Problem Solving: An Exercise in Synthetic Reasoning , 1977, IJCAI.

[9]  B. Chandrasekaran,et al.  A Mechanism for Forming Composite Explanatory Hypotheses , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  Kevin D. Ashley,et al.  A case-based approach to modeling legal expertise , 1988, IEEE Expert.

[11]  John H. Holland,et al.  Induction: Processes of Inference, Learning, and Discovery , 1987, IEEE Expert.

[12]  B. Chandrasekaran,et al.  Towards a Taxonomy of Problem Solving Types , 1983, AI Mag..

[13]  P. Thagard,et al.  Computational Philosophy of Science , 1988 .

[14]  E. Ziegel,et al.  Artificial intelligence and statistics , 1986 .

[15]  W. Bean.  Patterns of Discovery : An Inquiry into the Conceptual Foundations of Science. , 1960 .

[16]  Drew McDermott,et al.  Introduction to artificial intelligence , 1986, Addison-Wesley series in computer science.

[17]  Eugene Charniak,et al.  Motivation Analysis, Abductive Unification, and Nonmonotonic Equality , 1988, Artif. Intell..

[18]  William F. Punch,et al.  DESIGN CONSIDERATIONS FOR PEIRCE, A HIGH-LEVEL LANGUAGE FOR HYPOTHESIS ASSEMBLY. , 1986 .

[19]  David J. Spiegelhalter,et al.  Local computations with probabilities on graphical structures and their application to expert systems , 1990 .

[20]  Nuel D. Belnap,et al.  The logic of questions and answers , 1976 .

[21]  P. Miller.  Expert critiquing systems , 1986 .

[22]  Terry Winograd,et al.  Extended Inference Modes in Reasoning by Computer Systems , 1990, Artif. Intell..

[23]  Marek J. Sergot,et al.  The British Nationality Act as a logic program , 1986, CACM.

[24]  John McCarthy,et al.  SOME PHILOSOPHICAL PROBLEMS FROM THE STANDPOINT OF ARTI CIAL INTELLIGENCE , 1987 .

[25]  Wayne C. Curtis.  Statistical Concepts for Attorneys: A Reference Guide , 1983 .

[26]  T. Kuhn,et al.  The Structure of Scientific Revolutions , 1963 .

[27]  P. Strawson,et al.  Skepticism and Naturalism: Some Varieties. , 1984 .