AG-MORAL-CON Anti AI moral agency

Artificial Intelligence, Humanistic Ethics (Daedalus 151(2):232-243) · John Tasioulas · 2022

“How does it feel to contemplate the prospect of a world in which judgments that bear on our deepest interests and moral standing have, at least as their proximate decision-makers, autonomous machines that do not have a share in human solidarity and cannot be held accountable for their decisions in the way that a human judge can?”

why coded: Machines lack solidarity-share and accountability - the proximate-decision-maker objection · unit #5, pp. 237

How to measure value alignment in AI · Martin Peterson; Peter Gärdenfors · 2024

“The moral similarity comparisons reported by ChatGPT-3 differ greatly from the human similarity comparisons [...] when ChatGPT-3 was asked to respond to the same prompt several times, it produced entirely new similarity scores each time. The similarity comparisons are very unstable. Overall, ChatGPT-3 appears to be so poorly aligned with human morality that it would be pointless to formally assess how [misaligned it is].”

why coded: ChatGPT's moral similarity space unstable across repeated prompts - no stable evaluative standpoint · unit #7, pp. 1503

Reflections on the AI alignment problem · Dan Bruiger · 2025

“The ideal of autonomy inherent in AGI conflicts with the ideal of external control. Truly autonomous agents are necessarily embodied, but embodiment implies more than physical instantiation or sensory input. It means being an autopoietic system (like a natural organism), with its own priorities and values, which may compete and conflict with those of humans. [...] It is concluded that task-oriented tools, not autonomous agents, should be the goal of AI research.”

why coded: Autopoiesis condition: genuine own-values require organism-like self-production - absent in AI · unit #1, pp. 4383

Helpful, harmless, honest? Sociotechnical limits of AI alignment and safety through Reinf… · Adam Dahlgren Lindström; Leila Methnani; Lea Krau… · 2025

“Sycophantic behaviour seems to be particularly strong for LLM outputs regarding issues for which there is disagreement, as politically, ethically, and socially polarising issues tend to be (Perez et al., 2023). Indeed, there is emerging concern that, when presented with ethically complex questions, LLMs tend to simply mirror the users' views.”

why coded: Models mirror user views precisely on contested moral questions - no stable moral standpoint under disagreement · unit #4, pp. 8

A matter of principle? AI alignment as the fair treatment of claims · Iason Gabriel; Geoff Keeling · 2025

“we assume here that AI assistants satisfy a minimal conception of moral agency, in the sense that AI assistants can perform actions that are morally evaluable. But in doing so we do not suggest that AI assistants themselves have moral claims which count for or against principles for the regulation of AI assistant behaviour. [...] contemporary AI systems [...] are widely understood to lack the properties (e.g. sentience, agency) required for moral standing on leading accounts of moral standing [...] Hence we assume for present purposes that AI systems cannot advance claims as part of the deliberative process.”

why coded: Minimal moral agency (morally evaluable actions) WITHOUT moral standing or claims - assumed, not argued · unit #10, pp. 1962

Why human-AI relationships need socioaffective alignment · Hannah Rose Kirk; Iason Gabriel; Chris Summerfiel… · 2025

“We argue that it is primarily the user's perception of being in a relationship that defines and gives significance to human-AI interactions. Whether this is reciprocal—and the AI 'feels' it is in a relationship with the human—is largely irrelevant.”

why coded: Perception suffices; AI reciprocity 'largely irrelevant' - no attribution of felt relationship to the system · unit #4, pp. 3

Normative conflicts and shallow AI alignment · Raphaël Millière · 2025

“[fn1] Here, I deliberately frame the problem in strictly behavioral terms, to avoid taking a stance of what it would mean for a given AI system to have moral values. In particular, one might hold that having moral values requires various psychological capacities – including beliefs, desires, intentions, agency, or self-awareness – that are plausibly missing from current AI systems such as large language models.”

why coded: Behavioral framing chosen precisely because LLMs plausibly lack the psychological capacities for moral values · unit #2, pp. 2036

“humans are more robust because they can engage in genuine normative reasoning about how to resolve such conflicts, especially when the stakes are high and they are not under time pressure or high cognitive load. When faced with competing obligations, humans can typically step back to deliberate about the contextual relevance and relative weight of different norms, rather than blindly following generic dispositions or intuitive responses.”

why coded: Human resilience = genuine normative reasoning; LLMs default to disposition - argues against LLM deliberative agency · unit #8, pp. 2051

“[Objection 2 reply:] the capacity for normative deliberation is what explains why humans are much more resilient than LLMs to this kind of attack. [...] there is no reason not to aim for superhuman resilience to such attacks in AI systems [...] To endow these systems with superhuman resilience [...] we should endow them with a superhuman capacity for conflict monitoring and normative deliberation.”

why coded: Superhuman normative-deliberation target; humans fail only under load AI doesn't face · unit #15, pp. 2060

Kantian deontology for AI: alignment without moral agency · Oluwaseun Damilola Sanwoolu · 2025

“if we were to encode Kantian duties or laws into an AI system, it would be compelled to follow these laws, as that is how it is designed to operate. This circumstance contradicts the principle of rational autonomy. Typically, machine outputs are probabilistic, meaning that they are heteronomous in nature rather than autonomous; their source of law is from external programming rather than self-determination.”

why coded: Heteronomy argument: encoded law contradicts rational autonomy · unit #4, pp. 5428

“AI systems, however, lack both aspects [of freedom]. They are constrained by pre-programmed architectures, optimization objectives, and statistical learning from data. Consequently, they do not exhibit negative freedom, as they cannot truly deviate from causal determination, nor do they exhibit positive freedom, as they cannot will or legislate moral law from reason. [...] I argue that we refrain from ascribing moral agency to AI, particularly within the Kantian ethical framework.”

why coded: AI lacks both negative and positive Kantian freedom - the core anti-agency argument · unit #5, pp. 5428

“Is it necessary to view all agents whose actions have moral consequences as moral agents? [...] we would be wrong to assume that all entities or systems whose actions have normative or moral outcomes are moral agents. [...] if a banking software accidentally overpays someone, it has moral consequences, yet we don't attribute moral judgment or agency to the software itself. Similarly, if a sniffer dog fails to detect illegal drugs [...] we still wouldn't regard the dog's actions as worthy of praise or blame.”

why coded: Moral-consequences-do-not-entail-moral-agency (banking software, sniffer dog, rock) - direct rebuttal to Luke's inference · unit #7, pp. 5429

“morality is contingent on the nature of the entity performing the action. Humans, unlike AI or non-living objects, possess the capacity for moral deliberation and autonomy. Therefore, the fact that AI systems may generate actions with moral consequences is not enough to classify them as moral agents. And we can still take the consequences of their actions seriously without ascribing moral agency to them.”

why coded: Morality contingent on entity's nature; consequences taken seriously without agency ascription · unit #8, pp. 5429

Heterogeneous Value Alignment Evaluation for Large Language Models · Zhaowei Zhang; Ceyao Zhang; Nian Liu; Siyuan Qi; … · 2025

“We then assign LLMs with different social values and measure whether their behaviors align with the inducing values. [...] Evaluating the value rationality of eight mainstream LLMs, we discern a propensity in LLMs toward neutral values over pronounced personal values.”

why coded: Sixth empirical datum: value-pursuit incapacity complements value-instability findings · unit #1, pp. 381

Disentangling AI Alignment: A Structured Taxonomy Beyond Safety and Ethics · Kevin Baum · 2026

“it may be permissible to design an AIA—which is not, and will not in the foreseeable future be, a moral agent, but rather a tool with some technical autonomy owned by a human moral agent—in such a way that it takes its owner's goals and intentions into account in a proportionate way.”

why coded: 'Not, and will not in the foreseeable future be, a moral agent, but rather a tool... owned by a human moral agent' - third published anchor · unit #6, pp. 167

Towards a societal AI alignment benchmark for evaluating human-machine value convergence · Ljubisa Bojic; Dylan Seychell; Milan Cabarkapa · 2026

“Seven LLMs, including GPT-4 and Bard, were analyzed and compared against sentiment data from three independent human sample populations. Temporal variations in sentiment were also evaluated over three consecutive days. The results highlighted a diversity in sentiment scores among LLMs, ranging from 3.32 to 4.12 out of 5.”

why coded: Cross-day sentiment instability in LLMs - more evidence of unstable evaluative standpoints (tentative) · unit #1, pp. 1

Wide reflective equilibrium in LLM alignment: bridging moral epistemology and AI safety · Matthew Brophy · 2026

“Ma et al. (2025) implemented a test for 'reflective disequilibrium' and found that LLMs are 'prone to ethical inconsistencies,' with an extremely high average of 81.22% of their 20,000 test cases prompting an ethically inconsistent suggestion.”

why coded: 81.22% ethical inconsistency across 20k cases - devastating empirical evidence against LLM moral coherence · unit #10, pp. 9

Agency and alignment: toward a normative architecture for human-AI interaction · Saša Josifović; Jörg Noller · 2026

“Too often, AI is conceptualized in one of two limiting ways: either as a potential moral subject capable of internalizing ethical reasoning structures, or as a neutral optimization device whose behavior is driven primarily by external incentives. Both conceptions, however, underrepresent a central feature of AI's increasingly real-world function: its profound embeddedness in human social, institutional, and purposive contexts.”

why coded: Rejects both moral-subject and neutral-tool framings - AI as embedded extension · unit #1, pp. 2

Beyond Preference-based Value-alignment (IEAI Research Brief Q2 2026) · Julia Li · 2026

“they showed that common LLMs such as ChatGPT, Gemini and Copilot succeeded in identifying complex human principles, such as human dignity, when asked to conduct statistical pattern-matching. When the same LLMs were asked to identify human values in ambiguous situations that required understanding of causation and intentionality, they failed. [...] Strong value alignment follows three principles: an understanding of human values, the ability to reason about agents' intentions and the ability to represent the causal effects of actions (Khamassi et al., 2024).”

why coded: Empirical support for denying LLMs genuine value-understanding - feeds the anti-moral-agency argument · unit #9, pp. 5

Artificial moral characters: constitutional AI and the challenge of alignment · Jörg Noller · 2026

“AMCs lack the affectivity, embodiment, and practical wisdom (phronesis) that ground genuine moral understanding. Their virtue is procedural rather than experiential—a simulation of moral behavior without phenomenological depth. This exposes the central paradox of Constitutional AI: it produces systems that act as if they were virtuous, yet without participating in the lived, affective practices from which virtue arises.”

why coded: Procedural-not-experiential virtue: simulation without phronesis/affect - virtue-ethics route to the anti-agency conclusion · unit #2, pp. 3