The paper argues that AI governance should rely on ISO-like interoperability protocols rather than solely on jurisdiction-specific laws. It proposes standardized AI nutrition labels that include metrics for bias, energy usage, and data provenance to enable machine‑readable risk communication across borders. These protocols aim to reduce regulatory fragmentation, lower barriers for SMEs, and build public trust while allowing modular evolution with technology.
By Azmine Toushik Wasi, Mst Rafia Islam, Mahfuz Ahmed Anik, Taki Hasan Rafi, Md Manjurul Ahsan, Dong-Kyu Chae
arXiv:2608. 12320v1 Announce Type: cross Abstract: This article reviews and updates the framework for accountability in AI based on account- ability ecosystems.
By Chris Percy, Artur d'Avila Garcez
arXiv:2606. 01417v1 Announce Type: new Abstract: Turkey's e-Government Gateway (e-Devlet) serves over 68 million registered users with more than 9,200 government services, and is increasingly integrating artificial intelligence into citizen-facing applications such as chatbot assistants and eligibility assessments.
By Ahmet Kaplan
arXiv:2606. 12437v1 Announce Type: cross Abstract: The increasing encroachment of artificial intelligence (AI) on social life raises significant risks for society, particularly within the infospheres created and controlled by companies such as Google, Facebook, Apple, and Amazon.
By Oren Perez, Nurit Wimer
arXiv:2607. 21345v1 Announce Type: new Abstract: Regulating activities where regulatees use autonomous and agentic AI is challenging.
By Chris Reed, Alex Austria, Anmol Bharuka, Pragnitha Mandava, Khushiya Mujawar, Luka Shakhkulashvili
The article "We'll Fix It Later": Education, AI, and the Deferral of Privacy in EdTech examines how educational technology platforms collect sensitive student data but often postpone privacy considerations until later stages of product development. Through 12 interviews and a policy audit of 48 platforms, the study finds that privacy is acknowledged yet deferred in favor of functionality, growth, and funding, with responsibility frequently shifted to cloud providers or downstream institutions. The audit reveals that many platforms lack clear AI disclosures and provide limited governance details, indicating a gap between data collection practices and privacy accountability.
By Meghna Manoj Nair, Rachel Greenstadt