Skip to main content

Advertisement

SpringerLink
Log in

Intelligent Computer Evaluation of Offender’s Previous Record

  • Published:
Artificial Intelligence and Law Aims and scope Submit manuscript

Abstract

This paper considers the problem of how to evaluate an offender’s criminal record. This evaluation is part of the sentencing process carried out by a judge, and may be complicated in the case of offenders with a heavy record. We give a comprehensive overview of the approach to an offender’s past record in various (Western) countries, considering the two major approaches: desert-based and utilitarian. The paper describes the determination of the parameters involved in the evaluation, and the construction of a decision support system to be used by a judge about to pass sentence in a criminal case. The system may be used as a stand-alone system, but can also be integrated as a component of a general sentencing support system, e.g. a statistical sentencing information system. The system is a knowledge-based system. The knowledge base (in rule-form) was created primarily by eliciting knowledge from experts (judges, lawyers, academics and probation officers), but also by applying statutory law, case law and legal authoritative texts. It is flexible in the sense, that a user can introduce his1 own preferences (expressed as numerical coefficients) and thereby apply different sentencing principles. The prototype is at present undergoing testing by some of the participating judges.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • ALI (2003). American Law Institute, Model Penal Code: Sentencing, Report, April 11, 2003.

  • ABA (1994). Criminal Justice Sentencing Standards, American Bar Association, 3rd edition.

  • Ashley K. D. (1990). Modeling Legal Argument. MIT Press, Cambridge MA

    Google Scholar 

  • Ashley K. D. (1992). Case-Based Reasoning and its Implications for Legal Expert Systems. Artificial Intelligence and Law 1: 113–208

    Article  Google Scholar 

  • Ashworth A. (2000). Sentencing and Criminal Justice, 3rd edition, London.

  • (1991). Knowledge-Based Systems and Legal Applications. Academic Press, London

    Google Scholar 

  • Bonczek R. H., Holsapple C. W. and Whinston A. B. (1980). Foundations of Decision Support Systems. Academic Press, London

    Google Scholar 

  • Chan J. (1991). A Computerized Sentencing Information System for New South Wales Courts. Computer Law and Practice 7(3): 137–150

    Google Scholar 

  • Cohen A. and Schild Uri J. (1993). A Legal Expert Neural Network. Proc. Tenth Israeli Symposium on Artificial Intelligence, 283–290. Tel-Aviv: IAAI Press.

  • Council of Europe (1992). Consistency in Sentencing: Recommendation, R (92) 17, Strasbourg. Reprinted in: Criminal Law Forum, vol.4, (1993), 355–392.

  • Department of Justice (1995). Increasing the Utility of the Criminal History Record: Report of the National Task Force, U.S. Department of Justice, Dec. 1995, NCJ–156922

  • Doob A. N. and Park N. W. (1987). Computerized Sentencing Information for Judges. Criminal Law Quarterly 30: 54–72

    Google Scholar 

  • Dreyfuss H. L. (1992). What Computers Still Can’t Do. MIT Press, Cambridge, Mass

    Google Scholar 

  • FSRC (2004). Federal Sentencing Research Center, http://guidelinelaw.com/products/fsrc.htm

  • Hacohen-Kerner Y. and Schild U. J. (1999). The Judge’s Apprentice. The New Review of Applied Expert Systems 5: 191–202

    Google Scholar 

  • Hassett P. (1993). Can Expert System Technology Contribute to Improved Bail Conditions. Int. J. Law and Information Technology 1: 144–188

    Article  Google Scholar 

  • Hohfeld W. N. (1919). Fundamental Concepts as Applied in Judicial Reasoning. Yale University Press, New Haven

    Google Scholar 

  • Hogarth J. (1988). Computer and the Law: Sentencing Database System, User’s Guide. LIST Corporation, Vancouver

    Google Scholar 

  • Home Office, (2001). Making Punishment Work: Report of a Review of the Sentencing Framework for England and Wales, HMSO, UK.

  • Hutchison, T.W., Yellen, D., Hoffman, P.B. and Young, D. editors (2001). Federal Sentencing Law and Practice, West Publishers.

  • Hutton N., Patterson A., Tata C. and Wilson J. (1995). Decision Support for Sentencing in a Common Law Jurisdiction. Fifth International Conference on Artificial Intelligence and Law (ICAIL-95), pp. 89–95. Washington D.C.: ACM Press.

  • Hutton N. and Tata C. (2000). Sentencing Reform by Self-Regulation: Present and Future Prospects of the Sentencing Information System for Scotland’s High Court Justiciary. Scottish Journal of Criminology 6: 37–51

    Google Scholar 

  • Jones, A.J.I. and Sergot. M.J. (1993). On the Characterisation of Law and Computer Systems: The Normative Systems Perspective. Ch. 12. in Deontic Logic in Computer Science: Normative System Specification, J.-J.Ch. Meyer and R.J. Wieringa (eds), John Wiley and Sons.

  • Kannai R. (1996). The Judge’s Discretion in Sentencing: Israel’s Basic Laws and Supreme Court Decisions, 30 Israel Law Review, pp. 276–315.

  • Keppens J. and Zeleznikow J. (2003). A Model-based Reasoning Approach for Generating plausible Crime Scenarios from Evidence, Proceedings of the 9th International Conference on Artificial Intelligence and Law (ICAIL-03), ACM Press, New York, 51–59.

  • Lovegrove A. (1999). Statistical Information Systems as a Means to Consistency and Rationality in Sentencing. International Journal of Law and Information Technology 7: 31–72

    Article  Google Scholar 

  • McCarty L. T. (1977). Reflections on Taxman: An Experiment in AI and Legal Reasoning. Harvard Law Review 90: 837–893

    Google Scholar 

  • McCarty L. T. (2002). Ownership: A case study in the representation of legal concepts. Artificial Intelligence and Law 10: 135–161

    Article  Google Scholar 

  • Marcus M. (2002). http://ourworld.compuserve.com/homepages/SMMarcus/DSStechnts.html

  • Marcus M. (2003). Archaic Sentencing Liturgy Sacrifices Public Safety: What’s Wrong and How We Can Fix It, 16 Federal Sentencing Reporter, 15–76.

  • Maxfield L.D. (2002). Prior Dangerous Criminal behavior and Sentencing under the Federal Sentencing Guidelines, 87 Iowa Law Review, pp. 669–696.

  • Miller M.L. (2004). Sentencing Reform: The Sentencing Information System Alternative to Sentencing Guidelines, in Tonry M. (ed.) The Future of Imprisonment in the 21 st century, Oxford University Press, 1–34.

  • Minnesota Sentencing Guidelines II B 101. [M.S.A. ch. 244 App.] www.msgc.state.mn.us/Guidelines/guide04.DOC

  • Mitchell T.M. (1997). Machine Learning, McGraw-Hill.

  • Murphy D., (2002). Inside the United States Sentencing Commission: Federal Sentencing Policy in 2001 and beyond, 87 Iowa Law Review, 359–394.

  • Rissland E. L. and Skalak D.B. (1989). Combining Case-Based and Rule-Based Reasoning: A Heuristic Approach. Proc. Eleventh Int. Joint Conference on Artificial Intelligence (IJCAI), Detroit, MI, Morgan Kaufmann, San Mateo, CA, 524–530.

  • Roberts J.V. (1997). The Role of Criminal Record in the Sentencing Process, 22 Crime and Justice, pp. 303–362.

  • Roberts J.V. (2002). Public Opinion and Sentencing Policy, in Tonry M., Rex S. (eds.) Reform and Punishment: The Future of Sentencing Devon, Willan, 18–39.

  • Rossi P.H., Waite E., Bose C.B. and Berk R., (1974). The Seriousness of Crimes: Normative Structure and Individual Differences, 39 Am. Soc. Rev., 1974, pp. 224–237

  • Schild Uri J. (1998). Decisions Support for Criminal Sentencing, Artificial Intelligence and Law, 6(4): pp. 151–202, Kluwer Publ.

  • Schild Uri J. (2000). Statistical Information Systems for Sentencing: The Israeli Approach. International Review of Law, Computers & Technology 14(3): 317–324

    Google Scholar 

  • Shapira M. (1990). Computerized Decision Technology in Social Service. International Journal of Sociology and Social Policy 10: 138–164

    Article  Google Scholar 

  • Simon E. and Gaes G. (1989). ASSYST -computer support for guideline sentencing, Second International Conference on Artificial Intelligence and Law (ICAIL-89). Vancouver: ACM Press, pp. 195–200.

  • Stranieri A., Zeleznikow J., Gawler M. and Lewis B. (1999). A Hybrid rule-neural approach for the automation of legal reasoning in the discretionary domain of family law in Australia. Artificial Intelligence and Law 7(2–3): 153–183

    Article  Google Scholar 

  • Tata C. (1998). The Application of Judicial Intelligence and ‘Rules’ to Systems Supporting Discretionary Judicial Decision-Making, Artificial Intelligence and Law 6(4): pp. 203–230, Kluwer Publ.

    Google Scholar 

  • Thomas D. A. (1982). Principles of Sentencing. Sweet and Maxwell, London

    Google Scholar 

  • USSG #4A1.1 United States Sentencing Guidelines, #4A1.1 (www.ussc.gov/2004guid/)

  • Van der Vinne J., van Zwol W. and Karnekamp M. (1998). A Sentencing Information System named ‘NOSTRA’. International Journal of Law and Information Technology 6: 230–234

    Article  Google Scholar 

  • Vitiello M. (2002), Book Review: Punishment and Democracy: A Hard Look at Three Strikes’ Overblown Promises, 90 Calif. L. Rev. 257 (2002).

  • von Hirsch A. (1981). Desert and Previous Convictions in Sentencing, 65 Minn. L. Rev., pp. 591–634

  • von Hirsch A. (1982). Constructing Guidelines for Sentencing: The Critical Choices for the Minnesota Sentencing Guidelines Commission, 5 Hamline Law Review, pp. 164–200

  • von Hirsch A. (1988). Federal Sentencing Guidelines: The United States and Canadian Schemes Compared. The Center for Research in Crime and Justice, New York

    Google Scholar 

  • Von Hirsch A. (2002). Record Enhanced Sentencing in England and Wales: Reflections on the Halliday Report’s Proposed Treatment of Prior Record, in Tonry M., Rex S. (eds.) Reform and Punishment: The Future of Sentencing, Devon, Willan.

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Bar Ilan University, Ramat-Gan, 52900, Israel

    Uri J. Schild

  2. Faculty of Law, Bar Ilan University, Ramat-Gan, 52900, Israel

    Ruth Kannai

Authors
  1. Uri J. Schild
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruth Kannai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Uri J. Schild.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Schild, U.J., Kannai, R. Intelligent Computer Evaluation of Offender’s Previous Record. Artif Intell Law 13, 373–405 (2005). https://doi.org/10.1007/s10506-005-5793-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10506-005-5793-y

Keywords

Search

Navigation