title: Artificial intelligence and legal expertise in public administration
metadata.name: dc.contributor.author
metadata.value: Talapina, Elvira V.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2022-02-09T13:14:15Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2022-02-09T13:14:15Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2021-12
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Talapina, Elvira V. 2021. “Artificial intelligence and legal expertise in public administration”. Vestnik of Saint Petersburg University. Law 4: 865–881.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/spbu14.2021.404
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/34783
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: The legal comprehension of artificial intelligence (AI) is at an initial stage, despite its history
  and practical applications. The purpose of the study is to assess the capacity of using AI for legal expertise in public administration. General legal expertise improves the quality of a
  normative legal act, anti-corruption expertise serves to reduce the propensity for corruption.
  The implementation of AI must be regulated, and a transparent certification system should
  be created. In public administration among the reference points for regulating AI, it is noted
  that the personal data-processing mode should take into consideration the specific features
  of each application of AI; the mode should be differentiated. The transparency of using AI
  should be indisputable. A particular complex issue for regulation is AI learning. Different
  types of expertise correspond to different lists of stakeholders and data-in-use. The application
  of AI for anti-corruption expertise will be most effective in relation to draft regulatory
  legal acts. AI is able to contribute to the legal certainty of an act, and AI anti-corruption
  expertise learning will reduce the number of participants in the process. To implement this,
  it is important to consider the following circumstances. Firstly, the ability to process big data
  affords the opportunity to “teach” AI in predicting corruption violations, which is important
  for anti-corruption expertise based on a modeled assumption. Secondly, special attention is
  required for AI learning in terms of expert subtleties in assessing legal certainty. Thirdly, the
  anti-corruption evaluation algorithm must be combined with existing classifiers of legal acts,
  glossaries. Anti-corruption expertise using AI makes it possible to minimize subjectivity,
  unify approaches in interpreting corruption factors, increase the overall quality of normative
  legal acts and meet legal and technical requirements under the condition of strict regulation
  of AI learning and usage.
metadata.lang: en_GB
metadata.name: dc.description.sponsorship
metadata.value: The article was written on the basis of the Russian Academy of National Economy and Public Administration
  under the President of the Russian Federation state assignment research program.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Law;Volume 12; Issue 4
metadata.lang: -
metadata.name: dc.subject
metadata.value: expertise
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: artificial intelligence
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: corruption
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: legal security
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: digitalization
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: public administration
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Artificial intelligence and legal expertise in public administration
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 865-881.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/34783/1/865-881.pdf
resources.format: Adobe PDF
resources.size: 700,55 kB