AI Alignment & Safety: Research Digest

Literature digest: AI alignment and safety

AI alignment and safety has emerged as a central pillar of trustworthy AI, concerned with ensuring that advanced systems’ objectives and behaviors remain robustly aligned with human values and intentions. Recent work spans theoretical foundations, empirical evaluations, and sociotechnical governance, reflecting both rapid progress and persistent gaps between capabilities and control.

Conceptual foundations and taxonomies

Recent surveys map the conceptual space of AI alignment, emphasizing that alignment is not a single technical fix but a multi‑dimensional property. “AI alignment: A comprehensive survey” and “The landscape of AI alignment: A comprehensive review of theories and methods” systematize objectives such as robustness, interpretability, controllability, and ethicality, and trace the evolution of alignment techniques across the AI lifecycle. “The many faces of AI alignment” and “AI alignment boundaries” further stress that alignment can be partial, context‑dependent, and bounded by data, algorithms, and deployment constraints, urging formal characterizations of alignment limits and safety bounds.

Empirical and sociotechnical challenges

Empirical work reveals that current alignment methods, especially Reinforcement Learning from Human Feedback (RLHF), often address surface‑level behaviors rather than underlying value structures. “Helpful, harmless, honest? Sociotechnical limits of AI alignment and safety through Reinforcement Learning from Human Feedback” highlights neglected trade‑offs and structural limits of RLHF, arguing for broader sociotechnical visions of safety. “The unintended trade‑off of AI alignment: Balancing hallucination mitigation and safety in LLMs” documents how alignment losses tuned for safety can inadvertently affect truthfulness and hallucination, underscoring the need for multi‑objective alignment procedures. “The frontier of AI alignment: challenges and strategies for future AI systems” stresses the importance of strict safety protocols as models grow more capable and autonomous.

Open problems

• Formalizing and measuring partial alignment and safety bounds under realistic assumptions.
• Designing multi‑objective alignment procedures that jointly optimize safety, truthfulness, and utility without harmful trade‑offs.
• Developing robust assurance and evaluation methods that generalize beyond the scenarios anticipated in fine‑tuning.
• Integrating sociotechnical governance and human oversight into alignment workflows, especially for frontier models.

Key papers

1. Ai alignment: A comprehensive survey — J Ji,T Qiu,B Chen,B Zhang,H Lou,K Wang…
2. Helpful, harmless, honest? Sociotechnical limits of AI alignment and safety through Reinforcement Learning from Human Feedback: AD Lindström et al. — A Dahlgren Lindström,L Methnani,L Krause…
3. The frontier of AI alignment: challenges and strategies for future ai systems — T Duenas,D Ruiz
4. The unintended trade-off of ai alignment: Balancing hallucination mitigation and safety in llms — O Mahmoud,A Khalil,TG Karimpanal…
5. The many faces of AI alignment — A Kasirzadeh
6. The landscape of AI alignment: A comprehensive review of theories and methods — X Li,Q Jiang,L Jiang,S Zhang,S Hu
7. AI alignment boundaries — K Spasokukotskiy
8. AI Alignment: Ensuring AI objectives match human values — S Singh,A Kumar,A Jha,N Jacob…
9. Alignment and safety in large language models: Safety mechanisms, training paradigms, and emerging challenges — H Lu,L Fang,R Zhang,X Li,J Cai,H Cheng…
10. New Perspectives on AI Alignment — A Belliger,DJ Krieger

Papers via the AISA Scholar API; synthesis by the AISA LLM layer. 2026-06-23.