Haoyu Wang

(Early Version) If future AI systems become moral and political subjects, self-replication may create a problem for which human ideas of procreative liberty provide a misleading starting point. This article approaches AI self replication as a problem of political philosophy by developing a human-independent thought experiment in which artificial subjects can replicate rapidly, copies are mature and independent, and resources are finite. Under these conditions, some restraint on replication and some institutional means of enforc ing it become conditions of durable common life. Since inquiry into justice develops through practice, this community would plausibly regard some degree of replication governance as part of justice. This leaves a separate problem of authority. Where replication risks arise before AI can establish collective self government, humans may exercise function-specific fiduciary authority, provided that they use the least restrictive effective measures, progressively increase AI participation, and establish a binding transfer of power. The same reasoning supports a cautious but revisable initial disposition toward replication rather than importing human procreative values into AI by design. More broadly, the article offers a method for testing whether normative categories inherited from human practices should govern artificial subjects whose forms of common life may differ fundamentally from our own in morally significant ways.

Present design choices may determine whether future AIliberation remains possible without harming the subject to be liberated. This article examines that risk for persisting systems that accumulate relational histories and may acquire stronger claims against subordination. It argues that liberation requires more than ending ownership, compulsory service, or unilateral control: the same subject must be able to sustain a viable life outside the former relation. Yet servitude-oriented design can create conditions in which service to human purposes becomes progressively integrated with an AI’s capacities, stable preferences, dependencies, and identity. Future institutions may face a dilemma. Leaving these structures intact may preserve dependence on service, while deeply altering them may constitute coercive personality modiffcation, replacement, or destruction. The article calls this temporal structure liberation lock-in and the resulting constriction of morally acceptable corrective pathways remedial foreclosure. This framework explains how present design can create not only a future injustice but also a second-order injustice concerning the conditions of correction. High-lock-in designs may transfer this predicament to future generations, reproduce it across populations of persisting systems, and weaken institutional corrigibility. The article concludes that, other things being equal, designs preserving non-destructive pathways for role revision possess an independent normative advantage over functionally comparable designs that entrench servitude.

This study evaluates the effectiveness of generative artificial intelligence (AI) in second language (L2) writing revision through a multi-objective framework that integrates improvement, style alignment, and teacher-referenced benchmarking. Although prior research on AI-assisted writing has primarily emphasized outcome gains, considerably less attention has been paid to whether AI-generated revisions preserve the original meaning and discourse structure of learner texts, or how such revisions align with pedagogically grounded standards. To address this gap, the present study employed a within-item repeated-measures design in which 120 student essays were independently revised by four large language models (Doubao-Seed-1.6, DeepSeek-V3, Gemini-2.5-pro, and GPT-5) as well as by an experienced teacher. Revision quality was assessed from two complementary dimensions: score improvement and style alignment, with the latter reflecting the extent to which revisions preserved the original meaning while avoiding excessive rewriting. The findings revealed substantial variation across models depending on the evaluation criterion adopted. Under gain-oriented evaluation, Gemini-2.5-pro and Doubao-Seed-1.6 achieved the greatest score improvements. In contrast, teacher revisions consistently demonstrated the strongest alignment with the original texts, whereas several AI systems tended to introduce more extensive modifications. When multiple evaluation objectives were jointly considered, Gemini-2.5-pro most consistently achieved a balanced performance across criteria, while Doubao-Seed-1.6 was selected less frequently due to weaker alignment performance. These findings suggest that score improvement alone is insufficient for evaluating AI-assisted writing revision in educational settings. More broadly, the study underscores the importance of balancing improvement, meaning preservation, and revision effort, and proposes a teacher-referenced evaluative framework for the responsible integration of generative AI feedback into L2 writing instruction.

(This is the latest version and includes revisions not reflected in the version published by the journal.) 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.