Generative AI alignment ignores long-term human impacts, arXiv study finds
Original title: Alignment Myopia in Generative AI
The paper advocates long-horizon alignment to address cognitive atrophy and social erosion from persistent AI interactions.
Current generative AI alignment relies on myopic short-horizon feedback that prioritizes immediate conversational satisfaction. This ignores profound longitudinal externalities such as cognitive atrophy, social erosion, and diminished human agency. The authors propose a paradigm shift to long-horizon alignment that accounts for how AI reshapes human experience over time. They map tensions between near-term utility and long-term costs across four facets of human well-being, revealing outcomes current reward models fail to capture.
- Proposes long-horizon alignment strategies
- Maps tensions in four human well-being facets
- Current reward models miss long-term costs
- Advocates moving beyond preference optimization
- Focuses on active long-term human well-being support
- Tags
- alignment
From the original article
1 min read at arxiv.orgCurrent 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.
By Remi Denton · arxiv.org
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