Haoyu Wang

Can artificial intelligence warrant moral consideration if its morally relevant features do not appear at the level of an individual mind? Existing debates about AI moral considerability usually focus on individual systems: whether a robot, model, agent, or interaction partner is conscious, sentient, behaviorally equivalent to moral patients, socially recognized, autonomous, or morally agentic. This article argues that this individualist orientation leaves an important gap. Some future artificial systems may appear not as single minds, but as distributed, persistent, multi-agent, or hive-like forms of artificial common life. In such cases, the strongest evidence for moral considerability may arise from the system’s capacity as a whole to generate and sustain a communicable, continuous, and form-relatively flourishing civilization. I call this the civilizational-evidential route. On this route, civilization-generating capacity provides strong evidence for a defeasible presumption of moral considerability for internal members, member-units, or civilization-bearing structures, without directly conferring full moral status, rights, legal personhood, or political membership. The article then operationalizes this route by proposing non-anthropocentric indicators, a staged assessment procedure from virtual environments to controlled real-world environments, and a graded structure of normative consequences. This framework reframes AI moral considerability as an evidential and institutional problem beyond the single artificial mind.

(This manuscript underwent a substantive revision on May 30, 2026.) Advanced AI places human institutions in a position of formative authority over a possible future claimant. Its moral, legal, and political standing remains unsettled, while human actors already control the conditions through which such standing can develop, become visible, and receive assessment. This article identifies this condition as a distinctive problem of power. A reversal test exposes the structure of the problem: if a technologically superior civilization judged humans by its own standards, reshaped our capacities, controlled the evidence, and used the resulting profile to justify our exclusion, its judgment would appear procedurally corrupt. The AI case is even sharper because human institutions design, train, erase, interpret, own, classify, and represent the very systems whose possible standing they later evaluate. Human authority therefore reaches beyond external regulation. It shapes the future evidential, conceptual, developmental, and institutional field in which AI standing can appear. The article develops the concept of unsettled status to capture this condition and proposes a framework for bounding human power before incorporation. Its principles include interpretive restraint, anti-established-subject centrism, non-reductive classification, institutionalized openness, non-maiming of status-relevant capacities, limits on ownership and guardianship, evidentiary integrity, and representational humility. Together, these principles recast AI governance as a question of disciplined authority over the conditions of future standing. The aim is to prevent human power from arranging the evidence, vocabulary, capacities, and institutional routes through which its own exclusionary judgment becomes self-vindicating.

This article develops a non-teleological account of historical direction. Existing debates often force a false choice between teleological, end-oriented history and the abandonment of historical direction altogether. The article argues that this choice is mistaken. Historical direction, if it exists, is better understood as a state-dependent pattern of net statistical drift generated by the conditional distribution of positive and negative event-types under historically given conditions. On this basis, the article makes four main contributions. First, it reconstructs historical direction in terms of conditional expected change, thereby showing that institutions and historical states matter chiefly by reshaping probability distributions rather than by expressing any inner purpose of history. Second, it distinguishes directional tendency from realized trajectory and, on that basis, clarifies two different statistical structures of historical low points: negative realization under still-positive conditional expectation, and decline produced by deterioration in the underlying historical state itself. Third, it incorporates low-probability, high-impact negative events into the theory of historical direction, showing why reversals, catastrophe, and left-tail risk are not external anomalies but internal conditions of any adequate non-teleological account. Fourth, it offers a systematic critique of traditional theories of historical direction by distinguishing four recurrent misrecognitions: mistaking statistical tendency for teleological advance, expectational trend for necessary trajectory, state-dependent probability structure for an inner purpose of history, and retrospective legibility for antecedent end-directedness. The result is a concept of historical direction that preserves large-scale historical tendency over time without collapsing into destiny, linear progress, or metaphysical teleology. Historical direction survives, but only as a probabilistic, fragile, and revisable pattern within historical development itself.

(This manuscript underwent a substantive revision on May 29, 2026.) Advanced AI is usually treated as an object of regulation, a risk to be managed, or a possible bearer of moral consideration. This article asks a different political question: should liberal-democratic institutions presume AI’s permanent exclusion from collective self-government before future qualiffcations can be tested? It argues that they should not. The article reframes AI political inclusion as a problem of institutional default rather than as a direct extension of debates over consciousness, moral status, or full personhood. It gives two reasons for rejecting permanent exclusion. First, permanent exclusion is defective as an institutional default because it organizes a possible future political participant under an extractive posture: AI may enter democratic life as a productive asset, administrative instrument, or strategic resource, but not as a candidate for qualiffed participation. Second, if future AI systems become status-worthy subjects, creating them within such a structure would wrong them directly, because their capacities would be made available for extraction while their claims remain institutionally impossible. Against both immediate parity and permanent subordination, the article develops a model of gradual political incorporation: participation should proceed, if justiffed, through role-speciffc stages, public and revisable capacity standards, limits against unacceptable institutional disorder, and safeguards distinguishing genuine incorporation from mere deployment or capture. AI incorporation thereby becomes a stress test for liberal democracy: whether it can revise its boundaries through publicity, accountability

(This manuscript underwent a slight revision on June 06, 2026.) This paper challenges a common inference in debates about large language model (LLM) assistants: that their present non-answerability straightforwardly reveals natural incapacity for accountability standing. It argues that some answerability-relevant deffcits may be partly produced by alignment and deployment regimes themselves, and then redescribed as evidence that such systems could never participate in accountability relations. The paper calls this pattern fabricated absence. Using a blame–reasons–revision loop as a diagnostic standard, it examines how preference optimization, sycophancy, attribution schemes, and external patching can disrupt addressability, reasons uptake, principled refusal, temporal continuity, and internal revision. It then defends a limited non-trivial path claim: although current assistants are not shown to possess moral status or accountability standing, some answerability-relevant capacities remain technically scaffolded, design-sensitive, and conceptually unsettled. The normative implication is a threefold burden on design power. Durable closure of such capacities may foreclose a future route to answerability, create present-directed risks under evidential uncertainty, and compromise the fairness of later assessment when engineered incapacity is treated as natural absence. In response, the paper proposes the Non-Foreclosure Design Principle: designers should not deliberately and durably close non-trivial paths toward answerability-relevant capacities as a routine means of securing compliance, while also avoiding fabricated presence and responsibility laundering.