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Pluralistic Alignment Over Time
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Pluralistic Alignment Over Time
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If an AI system makes decisions over time, how should we evaluate how aligned it is with a group of stakeholders (who may have conflicting values and preferences)? In this position paper, we advocate for consideration of temporal aspects including stakeholders' changing levels of satisfaction and their possibly temporally extended preferences. We suggest how a recent approach to evaluating fairness over time could be applied to a new form of pluralistic alignment: temporal pluralism, where the AI system reflects different stakeholders' values at different times.
Forward citations
Cited by 2 Pith papers
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Formal Methods Meet LLMs: Auditing, Monitoring, and Intervention for Compliance of Advanced AI Systems
Combines LTL formal methods with LLMs for auditing, predictive monitoring, and runtime intervention on temporally extended behavioral constraints, outperforming LLM baselines and reducing violations.
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Principles Do Not Apply Themselves: A Hermeneutic Perspective on AI Alignment
AI alignment to principles requires context-sensitive interpretive judgments, as substantial preference data involves unresolved conflicts, creating gaps between corpus-induced and deployment-induced evaluations.
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