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Article Volume 6 Issue 6 3191 - 3204 December 24, 2023

From Algorithms to Accountability: Deciphering Legal Responsibility in AI-Driven Systems

Lead author · Corresponding
Harsh Pratap Singh
Student at NMIMS Kirit P. Mehta School of Law, Mumbai, MH, India
Co-author
Shrijeta Pratik
Student at NMIMS Kirit P. Mehta School of Law, Mumbai, MH, India
View PDF Full text DOIhttps://doij.org/10.10000/IJLMH.116495
Abstract

As artificial intelligence (AI) continues to permeate various aspects of society, the need to establish clear legal frameworks for addressing issues of accountability and responsibility becomes paramount. This paper delves into the intricate relationship between algorithms and legal liability in the context of AI-driven systems. The rapid advancement of AI technologies, fuelled by complex algorithms, raises challenging questions about the ethical and legal implications of their deployment. In a recent publication from a well-known computing journal, the inquiry was raised regarding the applicable laws in the unfortunate event of a self-driving car causing harm to a pedestrian. This paper examines the broader issue of legal accountability concerning artificially intelligent computer systems. It explores the possibility of criminal liability, identifying potential recipients of such liability. Additionally, within the realm of civil law, the paper scrutinizes whether an AI program falls under the category of a product, making it subject to product design regulations, or if it is considered a service to which the principles of the tort of negligence are applicable. Furthermore, the analysis extends to the consideration of sales warranties in this context.

Type
Article
Information
International Journal of Law Management and Humanities, Volume 6, Issue 6, Page 3191 - 3204
DOI: https://doij.org/10.10000/IJLMH.116495
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CC BY-NC 4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution–NonCommercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/), which permits remixing, adapting, and building upon the work for non-commercial use, provided the original work is properly cited.
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Copyright © IJLMH 2026
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The views and opinions expressed in this manuscript are those of the author(s) alone and do not reflect the views, policies, or position of the Journal.

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