Aug 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
Abstract This paper examines the challenges posed by Generative Artificial Intelligence (AI) to cyber law and legal responsibility in India. It argues that the rapid generation and dissemination of synthetic text, images, audio and video complicate traditional approaches to responsibility, particularly where developers, deployers, users and intermediaries exercise different degrees of control over AI-related risks. The study analyses the interaction of the Information Technology Act, 2000, the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021, and the Digital Personal Data Protection Act, 2023, while considering data governance, intermediary liability, AI-generated digital evidence, cybersecurity, transparency and professional responsibility. It proposes a risk-sensitive, lifecycle-based framework in which legal duties correspond to the degree of control, foreseeable harm and institutional responsibility. Particular emphasis is placed on human oversight in AI-assisted adjudication, verification of AI-generated legal material, provenance of synthetic content, privacy and security by design, effective remedies, and institutional documentation through AI-use registers for high-impact applications. The paper concludes that India should promote responsible AI adoption through a human-centred cyber-law framework that balances technological innovation with privacy, authenticity, security and the integrity of legal institutions.
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
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026