Classification of artificial intelligence technologies to determine the civil liability. Issue 1 (February 2021)
- Record Type:
- Journal Article
- Title:
- Classification of artificial intelligence technologies to determine the civil liability. Issue 1 (February 2021)
- Main Title:
- Classification of artificial intelligence technologies to determine the civil liability
- Authors:
- Alekseev, A
Erakhtina, O
Kondratyeva, K
Nikitin, T - Abstract:
- Abstract: The paper confirms a conclusion that there is no unified approach to the issue of civil liability for the actions of artificial intelligence. The authors propose to consider fault-based liability, liability regardless of fault and liability founded on a risk-based approach. To determine a type of liability for the actions of AI, the authors outline a classification of AI technologies on four grounds: autonomy, self-learning, feature and availability of data recorders. According to the authors, the most promising approach to the legal regulation of liability for the functioning of artificial intelligence technologies is a risk-based approach. The conclusions presented in the paper testify to the relevance of the topic of the research and prove the beginning of the formation of a scientific understanding of civil liability for the actions of AI in Russia and abroad.
- Is Part Of:
- Journal of physics. Volume 1794:Issue 1(2021)
- Journal:
- Journal of physics
- Issue:
- Volume 1794:Issue 1(2021)
- Issue Display:
- Volume 1794, Issue 1 (2021)
- Year:
- 2021
- Volume:
- 1794
- Issue:
- 1
- Issue Sort Value:
- 2021-1794-0001-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-02
- Subjects:
- Physics -- Congresses
530.5 - Journal URLs:
- http://www.iop.org/EJ/journal/1742-6596 ↗
http://ioppublishing.org/ ↗ - DOI:
- 10.1088/1742-6596/1794/1/012001 ↗
- Languages:
- English
- ISSNs:
- 1742-6588
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5036.223000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 25290.xml