Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
This paper examines whether existing Indian tort and defamation principles can adequately determine parental responsibility when a minor uses generative artificial intelligence to create or disseminate defamatory content, particularly where the parent had no realistic opportunity to detect the conduct. Indian law does not make parents automatically liable for the torts of their children; responsibility depends on an independent showing of parental fault, ordinarily negligent supervision, judged by whether the parent knew or ought reasonably to have known of the risk. The paper tests this standard against six constructed factual scenarios and argues that generative artificial intelligence does not defeat the standard as such, but sharply increases the incidence of genuinely undetectable harm, a condition the paper terms digital invisibility. Where such invisibility is real, the paper argues that responsibility is more defensibly located in the due diligence obligations recently imposed on intermediaries handling synthetically generated information, and in an as yet undeveloped Indian conception of generative AI developer responsibility, than in any expansion of parental fault. Drawing on a comparative examination of France, Mauritius, and the United Kingdom, the paper proposes a judicially administrable "reasonable digital supervision" standard for Indian courts, evaluated against four alternative reform options.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
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