AI Alignment with Human Preferences examines how the rapid advancement of generative AI and large language models has intensified the need to align AI systems with human values, intentions, and societal expectations. Written by Manas Talukdar, the paper surveys the evolving landscape of AI alignment research, tracing both the technical foundations of alignment methodologies and the ethical, governance, and operational challenges associated with integrating human preferences into AI systems. Drawing from academic literature and industry practice, it explores key approaches including supervised fine-tuning, reinforcement learning with human feedback (RLHF), direct preference optimization (DPO), constitutional AI, and human-in-the-loop systems, while analyzing their trade-offs in scalability, performance, safety, and implementation complexity.

The paper argues that AI alignment is not solely a technical optimization problem, but a broader socio-technical challenge involving questions of value representation, accountability, fairness, cultural relativism, privacy, and long-term societal impact. It highlights how the growing scarcity of high-quality training data has elevated the importance of human feedback and expert judgment in shaping next-generation AI systems. At the same time, it examines emerging risks such as reward hacking, distribution shift, adversarial manipulation, value lock-in, and scalable oversight limitations, positioning alignment as a critical frontier for the safe deployment of increasingly capable AI systems.

Intended for researchers, policymakers, technologists, and industry practitioners, the paper provides a comprehensive overview of the current state of AI alignment while identifying future research directions in mechanistic interpretability, adaptive alignment, multi-agent systems, governance frameworks, and aligned AGI development. Rather than presenting a single dominant solution, the paper concludes that effective alignment will likely depend on combining multiple methodologies within carefully designed institutional, technical, and ethical frameworks capable of evolving alongside increasingly advanced AI capabilities.

Governing Artificial Intelligence in Education argues that the effectiveness of AI governance in education is shaped less by the strength of regulatory design and more by the capacity to implement, operationalize, and sustain policy within real institutional environments. The brief examines how the United States and the European Union approach AI governance through contrasting models—one decentralized and implementation-driven, the other structured and risk-based—and shows how both systems encounter similar limitations when policies are not translated into practice. Drawing on comparative analysis, it highlights how gaps in institutional capacity, data governance, and oversight mechanisms contribute to uneven outcomes, persistent inequities, and limited evidence of impact on learning and decision-making.
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This policy brief proposes a shift from principle-based governance toward implementation-focused frameworks that prioritize operational clarity, institutional readiness, and continuous evaluation. It outlines key policy priorities, including strengthening human and technical capacity, embedding accountability into decision-making processes, and establishing mechanisms to assess how AI systems affect educational outcomes over time. Intended for policymakers, education leaders, and institutional stakeholders, the brief positions AI governance not as a purely regulatory challenge but as a systems-level coordination problem, where effective outcomes depend on aligning policy design with the realities of educational practice.

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