arXiv:2509.04445v2 Announce Type: replace Abstract: Recent AI trends seek to align AI models to learned human-centric objectives, such as personal preferences, utility, or societal values. Using standard preference elicitation methods, researchers and practitioners build models of human decisions and judgments, to which AI models are aligned. However, standard elicitation methods often fail to capture the cognitive processes behind human decision making, such as heuristics or simplifying structured thought patterns. To address this failure, we take an axiomatic approach to learning cognitively
The increasing push for AI alignment and the limitations of current preference elicitation methods are driving the need for more sophisticated approaches to understanding human cognition.
Improving AI alignment by incorporating a deeper understanding of human cognitive processes could lead to more robust, ethical, and trustworthy AI systems, reducing unintended outcomes.
The focus in AI alignment research shifts from purely behavioral preference elicitation to learning cognitively-faithful decision-making models, potentially influencing future AI development methodologies.
- · AI ethics researchers
- · Cognitive science researchers
- · Developers of foundational AI models
- · Industries deploying high-stakes AI
- · AI developers ignoring human factors
- · Organizations relying on simplistic alignment methods
AI models will begin to incorporate more nuanced representations of human decision-making, moving beyond simple utility functions.
This could lead to a new generation of AI agents that are more interpretable and adaptable to complex human contexts.
Improved AI alignment might reduce societal friction and enhance public acceptance of advanced AI, accelerating its integration across critical sectors.
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Read at arXiv cs.LG