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Human values are crucial to human decision-making. Value pluralism is theview that multiple correct values may be held in tension with one another(e.g., when considering lying to a friend to protect their feelings, how doesone balance honesty with friendship?). As statistical learners, AI systems fitto averages by default, washing out these potentially irreducible valueconflicts. To improve AI systems to better reflect value pluralism, thefirst-order challenge is to explore the extent to which AI systems can modelpluralistic human values, rights, and duties as well as their interaction.  We introduce ValuePrism, a large-scale dataset of 218k values, rights, andduties connected to 31k human-written situations. ValuePrism's contextualizedvalues are generated by GPT-4 and deemed high-quality by human annotators 91%of the time. We conduct a large-scale study with annotators across diversesocial and demographic backgrounds to try to understand whose values arerepresented.  With ValuePrism, we build Kaleido, an open, light-weight, and structuredlanguage-based multi-task model that generates, explains, and assesses therelevance and valence (i.e., support or oppose) of human values, rights, andduties within a specific context. Humans prefer the sets of values output byour system over the teacher GPT-4, finding them more accurate and with broadercoverage. In addition, we demonstrate that Kaleido can help explain variabilityin human decision-making by outputting contrasting values. Finally, we showthat Kaleido's representations transfer to other philosophical frameworks anddatasets, confirming the benefit of an explicit, modular, and interpretableapproach to value pluralism. We hope that our work will serve as a step tomaking more explicit the implicit values behind human decision-making and tosteering AI systems to make decisions that are more in accordance with them.

Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties