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Paper argues LLMs lack agency and moral responsibility

By PulseAugur Editorial · [2 sources] ·

A new academic paper argues that current large language models (LLMs) do not possess genuine agency or moral responsibility. The authors contend that LLMs operate based on probabilistic data mappings, lacking intrinsic intentionality and the capacity to own their outputs as commitments. While LLMs can produce coherent and normatively evaluable text, their apparent intentionality is derived, and stochastic sampling does not equate to choice or authorship, thus disqualifying them as moral agents. AI

IMPACT Challenges the notion of AI agency, potentially influencing ethical frameworks and public perception of LLM capabilities.

RANK_REASON The cluster contains an academic paper discussing LLM capabilities and philosophical implications.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Joseph Keshet ·

    Why Sampling Is Not Choosing: Intentionality, Agency, and Moral Responsibility in Large Language Models

    arXiv:2606.13441v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have prompted claims that such systems exhibit agency or qualify as moral agents. This paper argues that these attributions are misguided. We maintain that moral responsibility require…

  2. arXiv cs.AI TIER_1 English(EN) · Joseph Keshet ·

    Why Sampling Is Not Choosing: Intentionality, Agency, and Moral Responsibility in Large Language Models

    Recent advances in large language models (LLMs) have prompted claims that such systems exhibit agency or qualify as moral agents. This paper argues that these attributions are misguided. We maintain that moral responsibility requires commitment-bearing agency grounded in intrinsi…

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