THE EL-RAKHAWI THEORY OF NEURO-JURIDICAL SOVEREIGNTY IN THE AGE OF ARTIFICIAL INTELLIGENCE A Comparative Framework for Protecting Cognitive Rights Between the European Union and Emerging Models
Oct 2026· Zenodo (CERN European Organization for Nuclear Research)
Digitalization, Law, and Regulation
Abstract
The El-Rakhawi Theory of Neuro-Juridical Sovereignty in the Age of AI (DOI: 10.5281/zenodo.23271595) presents a groundbreaking comparative legal framework protecting cognitive rights between EU models and Global South approaches. It establishes that neural data transcends traditional privacy categories, requiring constitutional-level protection as "inalienable rights" where systemic prohibition replaces individual consent. Central is the Neuro-Juridical Sovereignty Index: NJSI=(M×L×T×E)/[(M×L×T×E)+V×A+ε], where legal variables are operationally defined as normalized composite indices. Under the Weakest-Link Axiom and Asymptotic Perfection Principle (max ≈0.9901), it mathematically proves that cognitive protection collapses without simultaneous optimization of mental privacy, legal robustness, technological transparency, and ethical governance. The theory proposes a Universal Declaration of Cognitive Rights, mandatory Cognitive Impact Assessments, and Independent Neuro-Ethics Tribunals. It criminalizes covert cognitive manipulation and establishes neural data minimization principles for BCIs. Released under the Ferial Zayed Humanitarian License as the definitive blueprint for protecting human consciousness in the algorithmic age.
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