MAP: Multi-Human-Value Alignment Palette

Ensuring that generative AI systems align with human values is essential but challenging, especially when considering multiple human values and their potential trade-offs. Since human values can be personalized and dynamically change over time, the desirable levels of value alignment vary across different ethnic groups, industry sectors, and user cohorts. Within existing frameworks, it is hard to define human values and align AI systems accordingly across different directions simultaneously, such as harmlessness, helpfulness, and positiveness. To address this, we develop a novel, first-principle approach called Multi-Human-Value Alignment Palette (MAP), which navigates the alignment across multiple human values in a structured and reliable way. MAP formulates the alignment problem as an optimization task with user-defined constraints, which define human value targets. It can be efficiently solved via a primal-dual approach, which determines whether a user-defined alignment target is achievable and how to achieve it. We conduct a detailed theoretical analysis of MAP by quantifying the trade-offs between values, the sensitivity to constraints, the fundamental connection between multi-value alignment and sequential alignment, and proving that linear weighted rewards are sufficient for multi-value alignment. Extensive experiments demonstrate MAP's ability to align multiple values in a principled manner while delivering strong empirical performance across various tasks. Recent advancements in artificial intelligence (AI) have highlighted the critical need for aligning AI systems with human values, a concept known as human value alignment (Griffith et al., 2013; Arumugam et al., 2019; Gabriel, 2020). The alignment can serve the purpose of generating outcomes that are better suited for human ethics (Griffith et al., 2013), personalized needs (Kirk et al., 2024), or reduced harmful content (Bai et al., 2022). This alignment has traditionally been pursued by adjusting AI behavior to adhere to specific attributes via preference datasets or reward functions. This formulation has deep conceptual roots in the Bayesian decision theoretic framework (Bissiri et al., 2016).