Computational Indicators in the Legal Profession: Can Artificial Intelligence Measure Lawyers' Performance?

David Restrepo-Amariles, Pablo Marcello Baquero, Paul Boniol, Rajaa El Hamdani () and Michalis Vazirgiannis
Additional contact information
David Restrepo-Amariles: HEC Paris - Ecole des Hautes Etudes Commerciales
Pablo Marcello Baquero: HEC Paris - Ecole des Hautes Etudes Commerciales
Paul Boniol: University of Paris
Rajaa El Hamdani: HEC Paris - Ecole des Hautes Etudes Commerciales

Working Papers from HAL

Abstract: The assessment of the legal professionals' performance is increasingly important in the market of legal services to provide relevant information both to consumers and to law firms regarding the quality of legal services. In this article, we explore how computational indicators are produced to assess lawyers' performance in courtroom litigation, analyzing the specific types of information they can generate. We capitalize on artificial intelligence (AI) methods to analyze a sample of 8,045 cases from the French Courts of Appeal, explore different associations involving lawyers, courts, and cases, and assess the strengths and flaws of the resulting metrics to evaluate the performance of legal professionals. The methods we use include natural language processing, machine learning, graph mining and advanced visualization. Based on the examination of the resulting analytics, we uncover both the advantages and challenges of assessing performance in the legal profession through AI methods. We argue that computational indicators need to address deficiencies regarding their methodology and diffusion to users to become effective means of information in the market of legal services. We conclude proposing adjustments to computational indicators and existing regulatory tools to achieve this purpose, seeking to pave the way for further research on this topic.

Keywords: Law and technology; artificial intelligence; legal informatics; machine learning; NLP (search for similar items in EconPapers)
Date: 2021-06-30
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:hal-03885355

Access Statistics for this paper

More papers in Working Papers from HAL
Bibliographic data for series maintained by CCSD ().