Similarity in harder cases: sentencing for fraud

ABSTRACT

We focus on one of the central concepts of case-based reasoning: similarity. In the field of sentencing, where the really decided cases are often on the harder side, similarity is multidimensional and depends less on formal rules than on various legitimate principles, objectives and factors which relate to the offender, the victim, the act and its social context. The paper presents our data base of empirically analysed cases of fraud and discusses two of the different phases completed to reduce complexity: (1) a decision topology whose categories emerged from in-depth interviews with judges, Crown and defense attorneys and which proved to be statistically significant; (2) experimenting with FXS, a program designed to retrieve similar cases based on their degree of salience. The salience coefficient used is a measure of similarity based on relative high or low frequency of factors in their local context. Particularly promising, although not yet formally explained, are FXS' flexible weighting possibilities.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.1 Applications and Expert Systems
          Subjects: Law

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.3 Deduction and Theorem Proving
          Subjects: Uncertainty, "fuzzy," and probabilistic reasoning

General Terms:
Legal Aspects, Theory