Alignment Myopia in Generative AI

Remi Denton; Aida Davani; Cynthia L Bennett; Michael Madaio; Roma Patel; Himabindu Lakkaraju

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arXiv · 2611.06830

Alignment Myopia in Generative AI

Remi Denton·Aida Davani·Cynthia L Bennett·Michael Madaio·Roma Patel·Himabindu Lakkaraju

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Abstract

Current generative AI alignment relies on a myopic paradigm that prioritizes immediate conversational satisfaction. By relying on short-horizon feedback, this approach ignores the profound, longitudinal externalities of persistent human-AI interaction, which can lead to adverse outcomes such as cognitive atrophy, social erosion, and diminished human agency. To address this, we propose a paradigm shift toward long-horizon alignment that accounts for how AI reshapes human experience over time. To this end, we map the tensions between near-term utility and long-term costs of AI use across four facets of human well-being to reveal outcomes that current reward models fail to capture. Ultimately, we advocate for moving beyond simple preference optimization toward alignment strategies that actively foster long-term human well-being by navigating these complex temporal scales.

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Remi Denton, Aida Davani, Cynthia L Bennett, Michael Madaio, Roma Patel, Himabindu Lakkaraju. 2026-11-04. Alignment Myopia in Generative AI. https://arxiv.org/abs/2611.06830

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