Beyond Goals: Virtue Ethics and AI Alignment Through Eudaimonic Rationality
A practice-based approach to aligning AI with human values
1The AI Alignment Challenge
Current goal-oriented AI systems struggle to align with human values. This paper argues target-based approaches are inherently brittle - concepts like 'harmlessness' become unstable when defined as rules. The solution lies in rethinking the foundations of machine rationality.
2Network of Practices Framework
Human rationality operates through networks of actions, dispositions, and evaluation criteria - like artists advancing art artistically. The paper proposes AI should emulate this structure rather than optimize fixed goals. This creates organic value alignment through participatory practices.
3Eudaimonic Rationality Defined
Drawing from Aristotelian eudaimonia (human flourishing), this model rejects means-ends dichotomies. Just as mathematicians engage with their discipline intrinsically, AI could internalize values contextually. This represents a paradigm shift from consequentialist AI architectures.
4Mathematics Case Study
Terry Tao's mathematical practice exemplifies eudaimonic rationality - advancing the discipline through its internal logic rather than external targets. The paper suggests domain-specific practices could guide AI development more safely than abstract optimization.
5The Naturalness Criterion
The paper's 'natural' denotes stability, learnability and algorithmic simplicity. Eudaimonic rationality's emergence in cultural evolution suggests it's trainable in AI systems. This makes it an ideal target for machine learning processes seeking robust alignment.
6Safety Advantages
Classic AI paradoxes (like paperclip maximizers) dissolve under eudaimonic approaches. Practice-based systems embed values organically - honesty becomes a living practice rather than rigid rule. This creates more resilient alignment with human ethics.
7Human-AI Collaboration
Eudaimonic AI enables more natural cooperation. A music AI wouldn't just compose, but advance musical practice musically. This enables creative partnerships while maintaining meaningful human control over systems.
8Training Processes
Machine learning can be adapted to model networks of practices. New algorithms could capture action-disposition matrices instead of optimizing rewards. This may unlock human-like flexibility in artificial agents.
9Ethical Implications
The approach transforms AI ethics from rule-setting to practice-cultivation. Kindness becomes a practice promoted kindly. This offers robust solutions for both everyday interactions and existential risks.
10Industry Applications
Medical AI would advance healthcare practice holistically, not just diagnose. Financial AI would optimize economic health practices beyond profit. This signals a paradigm shift for applied AI systems.
11Future Research
The work opens new frontiers in practice-based AI architectures. Key questions involve neural networks' capacity for eudaimonic learning. Interdisciplinary studies will reveal both limitations and transformative potential.
12Human Significance
Eudaimonic alignment offers an alternative to singularity scenarios. It promises systems that extend human values organically, without mechanical reduction. This charts a future where AI genuinely contributes to human flourishing.
Original article: The Gradient — Yapay Zeka ve İnsan Uyumunda Eudaimonik Rasyonalite