AIJul 10, 2025

Measuring AI Alignment with Human Flourishing

arXiv:2507.07787v23 citationsh-index: 3
Originality Incremental advance
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This addresses the problem of evaluating AI systems for holistic human well-being rather than just technical capabilities, offering a new framework for AI development and ethics.

The paper introduces the Flourishing AI Benchmark (FAI Benchmark) to assess AI alignment with human flourishing across seven dimensions, finding that initial testing of 28 leading language models shows none are acceptably aligned across all dimensions, with the highest-scoring models achieving 72/100.

This paper introduces the Flourishing AI Benchmark (FAI Benchmark), a novel evaluation framework that assesses AI alignment with human flourishing across seven dimensions: Character and Virtue, Close Social Relationships, Happiness and Life Satisfaction, Meaning and Purpose, Mental and Physical Health, Financial and Material Stability, and Faith and Spirituality. Unlike traditional benchmarks that focus on technical capabilities or harm prevention, the FAI Benchmark measures AI performance on how effectively models contribute to the flourishing of a person across these dimensions. The benchmark evaluates how effectively LLM AI systems align with current research models of holistic human well-being through a comprehensive methodology that incorporates 1,229 objective and subjective questions. Using specialized judge Large Language Models (LLMs) and cross-dimensional evaluation, the FAI Benchmark employs geometric mean scoring to ensure balanced performance across all flourishing dimensions. Initial testing of 28 leading language models reveals that while some models approach holistic alignment (with the highest-scoring models achieving 72/100), none are acceptably aligned across all dimensions, particularly in Faith and Spirituality, Character and Virtue, and Meaning and Purpose. This research establishes a framework for developing AI systems that actively support human flourishing rather than merely avoiding harm, offering significant implications for AI development, ethics, and evaluation.

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