Two Black Boxes: Where AI-Generated Works Come to Mean — A Relational Reframing through Dependent Origination, Kamae, and Concordance

The black-box problem of artificial intelligence is usually understood as the opacity of a model's internal computation. This paper proposes that there is a second problem. The process by which an AI-generated work comes to mean something to a person is also not visible. I call the first the technical black box and the second the relational black box, and I argue that solving the technical black box does not close the relational one. The argument rests on a consequence of a previously published model of experience generation (Awata 2026): meaning generation is possible only within the phenomenal domain of a receiver, and the outputs of an AI system remain informational states that are nobody's experience. On this basis, I identify what on the receiver's side orients the formation of meaning as kamae, a posture that functions as a phase-selection mechanism, and I propose a structural model that describes the formation and change of meaning with three terms: concordance, de-concordance, and re-concordance. The model reads the well-documented drop in evaluation that follows an "AI-made" label as de-concordance, and the longitudinal shift from resistance to acceptance of generative AI as re-concordance, within a single framework. What the model adds to research on the reception of AI-generated works is a receiver-side condition and a set of predictions that the anthropocentric-bias account does not yield. Finally, I state three testable predictions, re-concordance under repeated exposure of the same receiver to the same work, individual differences in kamae, and the order of disclosure, and I discuss three anticipated objections. One of them, whether de-concordance can be distinguished from cognitive dissonance, is left open. The paper is a conceptual proposal. It does not claim that AI has consciousness or kamae, and it does not claim identity with quantum measurement or physical energy.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-28
DOI
https://doi.org/10.5281/zenodo.23729122
Primary Topic
Aesthetic Perception and Analysis
Type
preprint
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preprint

Two Black Boxes: Where AI-Generated Works Come to Mean — A Relational Reframing through Dependent Origination, Kamae, and Concordance

Masashi Awata
4 links
Zenodo (CERN European Organization for Nuclear Research)
Aesthetic Perception and Analysis
preprint

Two Black Boxes: Where AI-Generated Works Come to Mean — A Relational Reframing through Dependent Origination, Kamae, and Concordance

Masashi Awata
preprint en

Abstract

The black-box problem of artificial intelligence is usually understood as the opacity of a model's internal computation. This paper proposes that there is a second problem. The process by which an AI-generated work comes to mean something to a person is also not visible. I call the first the technical black box and the second the relational black box, and I argue that solving the technical black box does not close the relational one. The argument rests on a consequence of a previously published model of experience generation (Awata 2026): meaning generation is possible only within the phenomenal domain of a receiver, and the outputs of an AI system remain informational states that are nobody's experience. On this basis, I identify what on the receiver's side orients the formation of meaning as kamae, a posture that functions as a phase-selection mechanism, and I propose a structural model that describes the formation and change of meaning with three terms: concordance, de-concordance, and re-concordance. The model reads the well-documented drop in evaluation that follows an "AI-made" label as de-concordance, and the longitudinal shift from resistance to acceptance of generative AI as re-concordance, within a single framework. What the model adds to research on the reception of AI-generated works is a receiver-side condition and a set of predictions that the anthropocentric-bias account does not yield. Finally, I state three testable predictions, re-concordance under repeated exposure of the same receiver to the same work, individual differences in kamae, and the order of disclosure, and I discuss three anticipated objections. One of them, whether de-concordance can be distinguished from cognitive dissonance, is left open. The paper is a conceptual proposal. It does not claim that AI has consciousness or kamae, and it does not claim identity with quantum measurement or physical energy.

Zenodo (CERN European Organization for Nuclear Research)
Aesthetic Perception and Analysis
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