arXiv:2607.05052v1 Announce Type: cross Abstract: Human value detection is commonly formulated as sentence-level multi-label classification over the 19 refined Schwartz values, typically predicted as independent labels. Schwartz theory, however, describes them as a circular motivational continuum, in which adjacent values are compatible and opposing values are in tension. We ask whether this structure can be operationalized as an explicit output-space geometry and used as a soft bias rather than a hard constraint. On a DeBERTa-v3-base classifier, we compare two ways of injecting it: training-t
This paper leverages advanced AI techniques to refine the understanding and detection of human values, building on existing psychological theories with computational methods.
Improved human value detection in AI systems can lead to more nuanced and ethically aligned AI, impacting development across various applications.
The explicit operationalization of Schwartz's circular motivational continuum as an output-space geometry offers a new method for incorporating human values into AI classifiers beyond independent labels.
- · AI ethics researchers
- · NLP developers
- · AI companies focusing on alignment
- · Social science researchers
- · AI systems lacking value-alignment mechanisms
- · Simple multi-label classification approaches
AI models will become more sophisticated in understanding and categorizing human values, moving beyond simple keyword matching.
This improved understanding could lead to the development of AI systems that are inherently more empathetic and better at navigating complex social interactions.
Ethical AI development might accelerate, potentially influencing regulatory frameworks and public trust in AI technologies.
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Read at arXiv cs.AI