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LLM Alignment should go beyond Harmlessness–Helpfulness and incorporate Human Agency
Naseem, Usman Chakraborty, Tanmoy Chang, Kai-Wei Dras, Mark Nakov, Preslav Peng, Nanyun Poria, Soujanya
Naseem, Usman
Chakraborty, Tanmoy
Chang, Kai-Wei
Dras, Mark
Nakov, Preslav
Peng, Nanyun
Poria, Soujanya
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s12559-026-10568-9.pdf
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Natural Language Processing
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Journal article
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http://creativecommons.org/licenses/by/4.0/
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English
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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.
Citation
U. Naseem, T. Chakraborty, K.-W. Chang, M. Dras, P. Nakov, N. Peng , et al., "LLM Alignment should go beyond Harmlessness–Helpfulness and incorporate Human Agency," Cognitive Computation, vol. 18, no. 1, pp. 26-26, 2026, https://doi.org/10.1007/s12559-026-10568-9.
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Cognitive Computation
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46 Information and Computing Sciences, 4609 Information Systems, 16 Peace, Justice and Strong Institutions
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Springer Nature