Pluralistic Alignment

Pluralistic alignment in AI aims to develop models that reflect the diverse values and preferences of different user groups, moving beyond the limitations of aligning models to a single, averaged preference. Current research focuses on creating benchmarks and datasets representing diverse perspectives, developing methods like self-supervised alignment and multi-model collaborations to achieve this pluralism, and exploring different frameworks for understanding and operationalizing pluralism (e.g., Overton, steerable, distributional). This work is crucial for ensuring fairness, inclusivity, and responsible development of AI systems, impacting both the ethical considerations within the field and the practical deployment of AI in real-world applications.