REVIEW 3 major objections 5 minor 3 references
Machinic Surrogates: Human-Machine Relationships in Computational Creativity
T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash
Pith's one-line read This paper argues that 'co-creation' with AI is actually collaboration between human agents carried out through tools called 'machinic surrogates.'
desk verdict A useful 'machinic surrogates' vocabulary for distributed authorship in AI art, but the load-bearing claim that third-identity collaboration applies to human-tool interaction is asserted, not shown. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The central object is the 'machinic surrogate': an AI/ML tool that acts as a proxy for its human makers, transmitting their identity and decisions into the creative process while producing novel, sometimes unpredictable outcomes. The paper adapts the late-twentieth-century theory that artistic collaboration forges a 'third identity' that exceeds the individual artists, and applies it to human-tool relations by locating the tool's identity not in the machine or algorithm but in its human makers. It also situates AI-enabled tools on a spectrum between automation and autonomy, and distinguishes roles—toolmaker, user, curator, audience—that human agents adopt across a project's life cycle. The three case studies show variations of surrogacy: a surrogate for absent toolmakers, a surrogate for a present audience, and a chain of surrogacies behind a widely publicized AI portrait.
What would settle it
Conduct a controlled comparison of two versions of the same generative design tool, identical in interface, whose models are trained by different toolmakers on differently curated datasets; if the output distributions do not differ in ways traceable to those curation choices, the claim that users are collaborating with the toolmakers loses its observable support.
Extended reading notes
Core claim
The paper's central claim is that applications of AI and machine learning in creative practice are best understood as a form of collaboration between human agents mediated by technical artifacts such as algorithms and data. It introduces the term 'machinic surrogate' to describe an AI-enabled tool that carries the identity of its human makers—called the 'human inspirers'—into a creative encounter, so that a user who appears to co-create with a machine is actually interfacing with the toolmakers through the surrogate. The machine's agency is derived, not autonomous: it is a re-creation of its makers' skills, decisions, and biases, situated in the algorithm and its hardware with all their limitations. If this is right, claims of autonomous machine creativity are incomplete, and authorship of computational artworks should be distributed across the human agents whose choices are inscribed in the tool.
Load-bearing premise
The argument collapses if a tool cannot inherit and transmit its makers' identity strongly enough for a genuinely new collaborative identity to emerge between a user and that inherited presence.
Editorial extensions
If this is right
- If the paper is right, the phrase 'human-machine co-creation' is misleading: the machine's contributions are the residue of human choices, so the active partners are all human.
- Authorship of an AI artwork should be distributed along the toolmaking chain—from the developers of the base model to the artists who retrain or modify it—rather than credited to the machine or to the end user alone.
- Users of an AI tool are working within a design space set by absent toolmakers; their freedom is real but bounded by decisions about architecture, data, and objectives made before the encounter.
- The same artifact can switch roles: it can act as a surrogate for absent toolmakers, or, as in an installation retrained by live audience input, as a surrogate for the present audience.
- Judging an AI artwork's quality therefore also means judging a human chain of curatorial and evaluative choices, not just the output of a probabilistic model.
Reading between the lines
- A testable design consequence follows: co-creative tools that make the toolmakers' choices visible—datasets, objectives, model architectures—would make the human collaboration legible, while black-box interfaces would obscure it.
- The surrogacy account has a natural limiting case: if algorithms begin designing algorithms, each iteration dilutes the original human agency, so the concept would eventually stop describing the creative relationship.
- If courts or markets ever assign authorship to AI-generated art, the surrogacy view suggests the relevant human contributions include the toolmaking chain, which could reshape licensing and provenance practices.
- One could probe the claim empirically: measuring whether users and audiences can detect different toolmakers' choices in generated outputs would test how strongly the surrogate actually transmits identity.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper argues that co-creation with AI-enabled tools in art and design is better understood not as a partnership between human and machine agents but as a form of human-human collaboration mediated by technical artifacts. It introduces the term "machinic surrogate" to describe how algorithms and datasets carry the decisions, skills, and biases of toolmakers into a creative interaction. The argument proceeds by reviewing computational creativity and co-creation discourses, drawing on late-twentieth-century theories of artistic collaboration (Green, Lévi-Strauss), and presenting three case studies—DeepCloud, My Artificial Muse, and Learning to See (Hello, World!)—as instances of machinic surrogacy. A fourth example, the Obvious team's Portrait of Edmond Belamy, is used to trace a chain of surrogacies among developers, artists, curators, and audiences. The conclusion extends the frame to future AI systems and notes the limitation that the human contribution may decay in algorithms that generate algorithms.
Significance. If the central claim holds, the paper makes a useful critical intervention in the HCI and computational creativity literatures: it shifts attention from the supposed autonomy of creative machines to the socio-technical networks of human agency embedded in them. The three case studies are concrete, relevant, and clearly described, and the proposed term "machinic surrogate" gives designers and researchers a vocabulary for discussing authorship and agency that avoids both naive tool-instrumentalism and exaggerated machine-autonomy claims. The paper also offers a valuable discussion of roles (toolmaker, artist-user, curator, audience) and of the authorship and ownership debates surrounding AI art. Its main weakness is that the central theoretical step—applying the artistic-collaboration model to human-machine interaction—is asserted rather than fully argued, and the case studies are selected to fit the frame without any negative or contrasting cases. The contribution is therefore significant as a conceptual provocation, but its general conclusion is not yet supported as stated.
major comments (3)
- [Human-machinic surrogate collaboration] The paper's central claim is that co-creation with AI is human-human collaboration mediated by "machinic surrogates," but this section explicitly states that artistic collaboration "is not directly applicable" to human agents and CC tools, and the subsequent bridge—asserting that tools carry a "derived identity" from their toolmakers—is not developed into a mechanism. Specifically, the manuscript does not specify how a derived identity can participate in the reciprocal, identity-eroding process by which Green and Lévi-Strauss define the emergence of a "third identity," nor does it offer observable criteria for when a tool's embodied choices amount to such an identity rather than to mere constraint. Because the conclusion that "co-creation is a special case of collaboration" rests on this step, the argument is under-specified at its load-bearing point.
- [Machinic Surrogacy in Practice] All three case studies (DeepCloud, My Artificial Muse, and Learning to See) are presented as instances of machinic surrogacy, but no negative or contrasting case is considered, such as a generative system whose outputs are not plausibly traceable to toolmakers' decisions or an interaction where no human-human mediation occurs. The selection therefore shows that the frame can be applied to these examples, but not that it explains the range of human-machine co-creation; the paper should state the selection criteria and discuss at least one disconfirming case to support the general claim made in the conclusion.
- [Authorship and Ownership] The manuscript acknowledges that outputs are "often not predictable by the human partner" (Conclusion) but does not analyze whether this unpredictability constitutes a separate agency that would break the surrogacy relation. The strong claim in the conclusion that "it is the agency of the authors and toolmakers which is crystallized in the tool" is not defended against the alternative explanation that a trained model's emergent behavior contributes something not reducible to the toolmakers' intentions; the paper should specify a criterion for distinguishing surrogate agency from emergent machine behavior and apply that criterion to the case studies.
minor comments (5)
- [Computational Creativity] There is a grammatical typo in the sentence beginning "These tools are the outcomes of joint efforts by an assembly of human agents, constitutes of"—"constitutes" should read "consisting" or "composed."
- [Humans' Role in Collaboration with Machinic Surrogates] In the Obvious paragraph, the phrase "various hum in action" appears to be a typographical error; it likely should read "various humans in action."
- [Machinic Surrogacy in Practice] The Sketch RNN discussion contains the phrase "the audiences who were interactions through an online game" which should be corrected to something like "audiences who interacted through an online game."
- [References] Reference [10] is incomplete (missing author names and article title), [25] misspells "Kingma" as "Knigma," and [31] lists the authors as "D. E. David Ha" though the correct form is "David Ha and Douglas Eck."
- [Introduction] The term "machinic surrogate" is central to the paper but is never given an explicit one-sentence definition; adding a concise definition near its first use would improve clarity.
Circularity Check
No significant circularity: the central claim is a conceptual synthesis with independent case-study support; only minor self-citation is present.
full rationale
The paper's central claim is interpretive, not formally derived: it proposes the notion of 'machinic surrogates' to reframe co-creation with AI as a form of human-human collaboration mediated by technical artifacts. There is no equation, fitted parameter, or empirical prediction that reduces to an input; the case studies (DeepCloud, My Artificial Muse, Learning to See, and the Edmond Belamy portrait) are presented as evidence and illustrations, not as outputs forced by construction. The only self-citation appears in the premise, drawn from co-author Cardoso Llach's prior work, that designed technologies are 'enactments of human intent' (refs. [3, p. 149] and [8]). This premise is supportive and even load-bearing for the framing, but the paper does not rely on it exclusively: it offers independent reasoning that CC tools are outcomes of joint human efforts and that training data are curated by human agents, and the case studies concretely show toolmakers' choices confining or influencing the creative space. The paper's admission that artistic collaboration 'is not directly applicable' to human-machine relations identifies a genuine gap in the argument, but a gap in applicability is not circularity: the move to a 'derived identity' is a substantive, contestable extension rather than a reduction to the input. Overall, the derivation chain is self-contained as a qualitative theoretical argument, and the self-citation is minor and not load-bearing in a circularity sense.
Assumptions & free parameters
assumptions (3)
- domain assumption Artistic collaboration requires the emergence of a 'third identity' that exceeds the individual contributors.
- domain assumption AI-enabled tools are re-created from their toolmakers' skills, decisions, and biases, and can be understood as surrogates for those toolmakers.
- ad hoc to paper The three selected projects (DeepCloud, My Artificial Muse, Learning to See) are adequate to demonstrate the phenomenon of machinic surrogacy.
invented entities (1)
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Machinic surrogate
Cite this review
Pith. "Pith review of Machinic Surrogates: Human-Machine Relationships in Computational Creativity." pith.science (2026). https://pith.science/paper/XP4DJ3CC
@misc{pith2026190801133,
author = {Pith},
title = {Pith review of: Machinic Surrogates: Human-Machine Relationships in Computational Creativity},
year = {2026},
howpublished = {\url{https://pith.science/paper/XP4DJ3CC}},
note = {Machine review of arXiv:1908.01133}
}
read the original abstract
Recent advancements in artificial intelligence (AI) and its sub-branch machine learning (ML) promise machines that go beyond the boundaries of automation and behave autonomously. Applications of these machines in creative practices such as art and design entail relationships between users and machines that have been described as a form of collaboration or co-creation between computational and human agents. This paper uses examples from art and design to argue that this frame is incomplete as it fails to acknowledge the socio-technical nature of AI systems, and the different human agencies involved in their design, implementation, and operation. Situating applications of AI-enabled tools in creative practices in a spectrum between automation and autonomy, this paper distinguishes different kinds of human engagement elicited by systems deemed automated or autonomous. Reviewing models of artistic collaboration during the late 20th century, it suggests that collaboration is at the core of these artistic practices. We build upon the growing literature of machine learning and art to look for the human agencies inscribed in works of computational creativity, and expand the co-creation frame to incorporate emerging forms of human-human collaboration mediated through technical artifacts such as algorithms and data.
Reference graph
Works this paper leans on
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[3]
D. Cardoso Llach, Builders of the vision, software and imagination of design, New York: Routledge, 2015. [4] N. Davis, C.-P. Hsiao, K. Y. Singh, L. Li and B. Magerko, "Empirically studying participatory sense-making in abstract drawing with a co-creative cognitive agent," in Proceedings of the 21st International Conference on Intelligent User Interfaces, ...
work page 2015
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[18]
Will Machinic Art Lay Beyond Our Ability to Understand It?,
R. Penha and M. Carvalhais, "Will Machinic Art Lay Beyond Our Ability to Understand It?," in Proceedings of the 24th International Symposium on Electronic Art, Durban, South Africa, 2018. [19] A. Bidgoli and P. Veloso, "DeepCloud," in Recalibration: On imprecision and infidelity Paper proceedings book for the 2018 Association of Computer Aided Design in A...
arXiv 2018
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[31]
A Neural Representation of Sketch Drawings,
D. E. David Ha, "A Neural Representation of Sketch Drawings," arXive preprint, arXive ID: 1704.03477v4, 2017. [32] A. Kazmin, "An AI genre in its infancy questions the nature of art | Financial Times," Financial Times, 27 August 2018. [33] N. Bourriaud, S. Pleasance, F. Woods and M. Copeland., Relational Aesthetics, Dijon: Les presses du réel, 2002. [34] ...
arXiv 2017
Reviewed August 14, 2026 · model on record in the stance chip above.
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