LLM Alignment should go beyond Harmlessness–Helpfulness and incorporate Human Agency
Abstract
Abstract Large Language Models are transforming communication, research, and decision-making, but misalignment – when models diverge from human values, safety requirements, or user intent – poses serious risks. In this position paper, we argue that many alignment failures stem from operational choices in training and deployment. We posit that alignment should shift from static, post-training constraints toward dynamic, participatory approaches that safeguard pluralism, autonomy, and human flourishing. We outline forward-looking directions, including pluralistic evaluation, transparency, and the Flourishing–Justice–Autonomy (FJA) framework, and present a roadmap for advancing alignment research and practice.
Fields of Study & Subjects
Business Law and Ethics
Artificial Intelligence in Law
Medical Coding and Health Information
Publication details
- Journal / source
- Cognitive Computation
- Publisher
- Springer Science and Business Media LLC
- Volume
- 18
- Issue
- 1
- Publication date
- 13 March 2026
- Language
- EN