Han Li

Humanoid Buddhist robots are often discussed as novelties, as signs of a supposedly distinctive East Asian comfort with technology, or as prompts for metaphysical questions about whether a machine can become religious. This article argues that such framings miss the institutional and ritual problem that makes these cases sociologically significant. The more productive question is not whether robots can become monks, bodhisattvas, or ritual specialists in any ontological sense, but why religious institutions repeatedly place them in reception, explanation, sermon performance, guided meditation, and other carefully delimited forms of public pedagogy while stopping short of fully replacing human clerics. Building on scholarship on digital religion, mediation, ritualization, and bounded religious automation, the article develops the concept of bounded ritual automation. This concept highlights the selective delegation of visible, repetitive, and affective religious labor to machines together with the retention of high-risk forms of ritual authority by human actors. Through a comparative analysis of Kodaiji Temple’s android Kannon Mindar in Kyoto, Longquan Monastery’s robot monk Xian’er in China, and the use of Pepper in Japanese funerary settings, the article shows that East Asian Buddhist robotics does not represent frictionless technological substitution. Rather, it reveals calibrated strategies of institutional adaptation under conditions of demographic aging, temple decline, platformization, and state regulation. “Electronic Buddhas” are therefore best understood not as curiosities, but as diagnostic sites where authority, presence, labor, and legitimacy are renegotiated in contemporary East Asian religion.

Responsible AI in Business reframes the ethics of generative AI as a problem of organizational design and governance engineering. Rather than treating "trustworthy AI" as a set of abstract principles, the book argues that responsibility is enacted through standards, controls, documentation, evaluation, procurement, and assurance—mechanisms that decide what counts as evidence, whose concerns are actionable, and which trade-offs organizations accept. It develops an ethical and epistemic framework for enterprise GenAI, including inductive bias as corporate architecture, truth and justification in AI-mediated decision-making, governance of data sources (privacy, copyright, retrieval), fairness and value trade-offs, and transparency and provenance for synthetic content. The book concludes with a practical operating model for institutionalizing Responsible GenAI, integrating emerging regulatory expectations and management-system approaches into audit-ready, capability-based governance.

Large language models (LLMs) now generate fluent, assertion-shaped text that circulates through scientific communication, public discourse, and institutional decision-making. This development pressures a familiar philosophical question: if LLMs do not literally believe what they output, can their outputs nevertheless be true in the sense targeted by classical theories of truth? This paper argues that they can. We model LLMs as belief-less asserters: systems that produce assertionshaped, truth-evaluable contents while lacking the psychological and normative profile of genuine asserters. We separate semantic questions about truth from normative questions about assertion, and we use three truth-theoretic lenses with distinct roles: deflationism to show that truth does not conceptually depend on belief, correspondence to address aboutness, reference, and derived intentionality in socio-technical settings, and Davidsonian truth-theoretic semantics to explain how interpretation and compositional meaning apply to sentence-incontext outputs without treating the producer as a believing agent. We then integrate leading objections as constraints on warranted reliance rather than on truth itself. The upshot is a sharpened distinction between truth and truthfulness: LLM outputs can be true even when epistemic accountability must be reassigned to surrounding human and institutional actors.

Large language models (LLMs) often appear to vindicate a radical empiricist picture: train on vast corpora of experience-like text, and capacities emerge without explicit symbolic rules. Yet contemporary machine learning research repeatedly emphasizes that what is learned, how quickly it is learned, and how well it generalizes depend crucially on prior constraints: architectural structure, training objectives, optimization dynamics, and representational bottlenecks. These constraints constitute inductive biases in a precise, technical sense. This paper develops a philosophical argument that uses LLMs as a case study to reassess the classical tabula rasa thesis associated with Locke and its descendants. I defend two claims. First, even the most ``data-driven'' generative models are saturated with structural priors that make learning possible at scale; thus, their success cannot be straightforwardly read as a triumph of unstructured empiricism. Second, once this is appreciated, the rhetorical appeal of a ``blank slate'' conception of the infant mind weakens further: if artificial systems trained on orders of magnitude more linguistic input than any child still require rich inductive biases, it is implausible that human cognition begins wholly unstructured. I then show how contemporary debates about whether LLMs ``understand'' language recapitulate the older dispute about whether experience alone can generate semantics and conceptual structure. The upshot is a more balanced, non-caricatured empiricism: experience matters, but explanation must explicitly account for the learning system's prior structure.