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Deliberative Technology for Alignment

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arxiv 2312.03893 v1 pith:A2FDHGO4 submitted 2023-12-06 cs.CY cs.HC

Andrew Konya , Deger Turan , Aviv Ovadya , Lina Qui , Daanish Masood , Flynn Devine , Lisa Schirch , Isabella Roberts
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Deliberative Alignment Forum
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classification cs.CYcs.HC
keywords powerfulsystemsaligndeliberativehumanitytechnologywillalignment
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For humanity to maintain and expand its agency into the future, the most powerful systems we create must be those which act to align the future with the will of humanity. The most powerful systems today are massive institutions like governments, firms, and NGOs. Deliberative technology is already being used across these institutions to help align governance and diplomacy with human will, and modern AI is poised to make this technology significantly better. At the same time, the race to superhuman AGI is already underway, and the AI systems it gives rise to may become the most powerful systems of the future. Failure to align the impact of such powerful AI with the will of humanity may lead to catastrophic consequences, while success may unleash abundance. Right now, there is a window of opportunity to use deliberative technology to align the impact of powerful AI with the will of humanity. Moreover, it may be possible to engineer a symbiotic coupling between powerful AI and deliberative alignment systems such that the quality of alignment improves as AI capabilities increase.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Chain of Alignment: Integrating Public Will with Expert Intelligence for Language Model Alignment

    cs.HC 2024-11 conditional novelty 6.0 of 10

    The chain of alignment method derives model behavior rules from publicly supported objectives and yields an automated reward that tracks expert ratings of response alignment (r=0.841).

  2. Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges

    cs.AI 2025-07 reject novelty 1.0 of 10

    A broad survey of LLM alignment that catalogs objectives, benchmarks, SFT/RLHF/DPO methods, and safety challenges, without contributing new experimental or theoretical results.

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