Oct 2026· International Journal of Law Management & Humanities· Vol 9, pp. 2211-2222· 5 references
Digitalization, Law, and Regulation
Abstract
A company is deciding whether to acquire another business. An artificial intelligence system examines financial records, market data and compliance risks, and recommends that the deal should proceed. The directors approve it. Months later, the acquisition causes serious loss. The board signed the resolution, but the model shaped the information and the choice. Who was really responsible? This article examines that question through section 166 of the Companies Act, 2013. It argues that directors may use AI, just as they use lawyers, auditors and other advisers, but they must not allow assistance to become substituted judgment. The relevant legal question is not whether the model was perfect. It is whether the directors understood its purpose, considered its limits, questioned important assumptions and retained the practical ability to reject its recommendation. The article distinguishes routine assistance, decision support and decision substitution. It then explains how opacity, automation bias, defective data and fragmented vendor arrangements may weaken accountability. Drawing on Indian company law and comparative principles reflected in the European Union Artificial Intelligence Act and Indian financial regulation, the article proposes five safeguards: disclosure of material AI use, board-level competence, governance-level explanation, records and continuing model review, and non-delegable human responsibility. Corporate governance may become algorithmically informed, but it cannot become algorithmically unaccountable.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9
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