Generative AI technologies present immense opportunities but also pose significant challenges, particularly in combating misinformation and ensuring ethical use. This policy paper introduces a dual-output transparency framework requiring organizations to disclose AI-generated content clearly. The proposed system provides users with a choice between fact-based and mixed outputs, both accompanied by clear markers for AI generation. This approach ensures informed user interactions, fosters intellectual integrity, and aligns AI innovation with societal trust. By combining policy mandates with technical implementations, the framework addresses the challenges of misinformation and accountability in generative AI.

A community-centered AI governance framework for India's welfare system Samagra Vedika, proposing 50% beneficiary representation, local language interfaces, and hybrid oversight to reduce algorithmic exclusion of vulnerable populations.

This research examines how AI recommender systems can be regulated to balance economic innovation with consumer privacy.

This policy memorandum draws attention to the potential for bias and opaqueness in intelligent systems utilized in K–12 education, which can worsen inequality. The U.S. Department of Education is advised to put Title I and Title IV financing criteria into effect that require human oversight, AI training for teachers and students, and open communication with stakeholders. These steps are intended to encourage the responsible and transparent use of intelligent systems in education by enforcing accountability and taking reasonable action to prevent harm to students while research is conducted to identify industry best practices.

Current AI development, concentrated in the Global North, creates measurable harms for billions worldwide. Healthcare AI systems provide suboptimal care in Global South contexts, facial recognition technologies misidentify non-white individuals (Birhane, 2022; Buolamwini & Gebru, 2018), and content moderation systems fail to understand cultural nuances (Sambasivan et al., 2021). With 14 of 15 largest AI companies based in the US (Stash, 2024), affected communities lack meaningful opportunities to shape how these technologies are developed and deployed in their contexts. This memo proposes mandatory implementation of the Human-centered AI Assessment Framework (HAAF), requiring pre-deployment impact assessments, resourced community participation, and clear accountability mechanisms. Implementation requires $10M over 24 months, beginning with pilot programs at five organizations. Success metrics include increased AI adoption in underserved contexts, improved system performance across diverse populations, and meaningful transfer of decision-making power to affected communities. The framework's emphasis on building local capacity and ensuring fair compensation for community contributions provides a practical pathway to more equitable AI development. Early adoption will help organizations build trust while developing more effective systems, delivering benefits for both industry and communities.

The "Chips for Peace" initiative aims to establish a framework for the safe and equitable development of AI chip technology, drawing inspiration from the "Atoms for Peace" program introduced in 1953. While the latter envisioned peaceful nuclear technology, its implementation highlighted critical pitfalls: a partisan approach that prioritized geopolitical interests over inclusivity, fostering global mistrust and unintended proliferation of nuclear weapons. This review explores these historical lessons to inform a better path forward for AI governance. It advocates for an inclusive, multilateral model, such as the proposed International AI Development and Safety Alliance (IAIDSA), to ensure equitable participation, robust safeguards, and global trust. By prioritizing collaboration and transparency, "Chips for Peace" can avoid the mistakes of its predecessor and position AI chips as tools for collective progress, not division.

Weekly AI monitoring bulletins that disseminated

through official national and international channels aim to keep the public informed of both the positive and

negative developments in AI, empowering individuals to take an active role in safeguarding against risks while maximizing AI’s societal benefits.

The Grandfather Paradox in Artificial Intelligence (AI) describes a

self-perpetuating cycle where outputs from flawed AI models re-

enter the training process, leading to recursive degradation of

model performance, ethical inconsistencies, and amplified biases.

This issue poses significant risks, particularly in high-stakes

domains such as healthcare, criminal justice, and finance.

This memorandum analyzes the paradox’s origins, implications,

and potential solutions. It emphasizes the need for iterative data

verification, dynamic feedback control systems, and cross-system

audits to maintain model integrity and ensure compliance with

ethical standards. By implementing these measures, organizations

can mitigate risks, enhance public trust, and foster sustainable AI

development.

While artificial intelligence (AI) presents transformative opportunities across various sectors, current safety evaluation approaches remain inadequate in preventing misuse and ensuring ethical alignment. This paper proposes a novel two-layer evaluation framework based on Constitutional AI principles. The first phase involves developing a comprehensive AI constitution through international collaboration, incorporating diverse stakeholder perspectives through an inclusive, collaborative, and interactive process. This constitution serves as the foundation for an LLM-based evaluation system that generates and validates safety assessment questions. The implementation of the 2-layer framework applies advanced mechanistic interpretability techniques specifically to frontier base models. The framework mandates safety evaluations on model platforms and introduces more rigorous testing for major AI companies' base models. Our analysis suggests this approach is technically feasible, cost-effective, and scalable while addressing current limitations in AI safety evaluation. The solution offers a practical path toward ensuring AI development remains aligned with human values while preventing the proliferation of potentially harmful systems.