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REVIEW 3 major objections 4 minor 53 references

Creativity Reconsidered: Generative AI and the Problem of Intentional Agency

T0 review · 3 major / 4 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read The paper argues that intentional agency is not a necessary condition of creativity, and that generative AI systems that reliably produce novel and valuable outputs should count as creative.

desk verdict A clear philosophy proposal for a consistency-based definition of creativity; the empirical support is weak but the core idea is worth engaging. read the letter →

arxiv 2601.15797 v2 pith:IWTGFMBN submitted 2026-01-22 cs.AI

James S. Pearson , Matthew J. Dennis , Marc Cheong This is my paper
classification cs.AI
keywords creativityintentionalagencygenerativeAINewStandardDefinitionconsistencyrequirementconceptualengineeringalgorithmaversionproduct-firstapproach
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper challenges the widely held view that conscious intentional agency is a necessary condition of creativity. It argues that generative AI, which produces novel and valuable outputs without intentions, forces a revision of this condition. The proposed New Standard Definition treats an object as creative if it is novel, valuable, and produced by a system that can consistently generate such objects. This would make many generative AI systems creative while still excluding accidental one-off outputs. The stakes are practical: the definition affects not just philosophical classification but real-world judgments about praise, investment, and the value of AI-generated work.

What carries the argument

The central move is the New Standard Definition (NSD): an object is creative if it is (a) novel, (b) valuable, and (c) produced by a system that can consistently generate novel and valuable objects. This replaces the Intentional Agency Condition with a thin consistency requirement. The supporting machinery is a conceptual-engineering argument: concepts should be revised to serve their core social functions, and the core function of 'creativity' is identifying and endorsing reliable sources of novel and valuable products. A process-first orientation, by contrast, now distorts evaluation through producer-identity and effort heuristics.

What would settle it

Run a controlled experiment with identical AI-generated outputs in three conditions: unlabelled, labelled as AI-made, and labelled as AI-made with an explicit note that creativity requires no intentional agency. If the third condition does not reduce the devaluation seen in the second, the paper's claim that the IAC itself feeds algorithm-aversion is unsupported.

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Extended reading notes

Core claim

The paper argues that the Intentional Agency Condition (IAC), which requires creative outputs to result from consciously represented goals, should be dropped as a general necessary condition of creativity. In its place, the paper proposes a consistency requirement: an object is creative if it is novel, valuable, and produced by a system that can consistently generate such objects. The argument is functional: the language of creativity exists to identify, endorse, and encourage reliable sources of novel and valuable artefacts, and the IAC once served that function by blocking endorsement of accidental producers. With generative AI, the paper claims, the IAC now feeds biases that lead people t

Load-bearing premise

The argument rests on the empirical premise that the Intentional Agency Condition itself, rather than a more general distrust of non-human producers, is what causes people to undervalue AI outputs; the cited studies show the bias exists but do not show that this specific philosophical condition is its cause.

Editorial extensions

If this is right

  • If the NSD is accepted, generative AI systems that reliably produce novel and valuable outputs qualify as creative without needing consciousness or intention.
  • The consistency requirement preserves the anti-accident function of the old IAC: a lucky one-off output still does not count, because the source is not reliable.
  • In scientific, engineering, and financial domains, AI solutions can be called creative even when they lack expressive authenticity, since practical value does not depend on sincere personal expression.
  • The IAC remains in place locally: cognitive scientists studying human creativity must still exclude AI outputs, and legal responsibility still requires agency.
  • Natural processes such as evolution may also count as creative, because they are consistent sources of novel and valuable solutions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper does not test its causal claim; a direct experiment could present identical AI outputs labelled only as human-made, AI-made, or AI-made plus an explicit statement that creativity requires no intention, to see if the devaluation shrinks.
  • The consistency criterion shifts the definitional burden to what counts as a 'system' and how much consistency is enough; borderline cases like a random generator with a lucky filter or a one-hit wonder will need practical thresholds the paper leaves open.
  • Making creativity an endorsement term rather than a praise term implicitly reframes human creative praise as a special case, which could complicate existing social uses of the term more than the paper acknowledges.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper argues that the Intentional Agency Condition (IAC) should no longer be treated as a necessary condition of creativity at the general level. It develops a conceptual-engineering framework: the core social function of creativity talk is to identify, endorse, and encourage reliable sources of novel and valuable products; the IAC once served this function by excluding accidental or non-reproducible sources, but, the authors claim, it now feeds into algorithm-aversion biases that lead people to undervalue AI-generated outputs. On this basis they propose a New Standard Definition (NSD): an object is creative if it is novel, valuable, and produced by a system that can consistently generate novel and valuable objects. They retain the IAC in local contexts such as expressive authenticity, cognitive science, and law. Two corpus analyses (Google Ngram and News on the Web) are presented as evidence that ordinary language is already shifting toward AI-creativity ascriptions.

Significance. If the argument succeeds, it would offer a principled way to classify generative AI outputs as creative without collapsing the distinction between accidental and reliable production, while also acknowledging the local importance of intentional agency. The paper engages seriously with the main defenders of the IAC, provides a clear and testable definition, and makes a constructive proposal rather than merely criticizing existing views. Its strengths include the explicit contextualism, the distinction between praise and endorsement, and the attempt to ground conceptual revision in functional considerations. However, the empirical and causal load placed on the corpus analyses and the algorithm-aversion literature is substantial, and those parts are not currently strong enough. The central normative thesis is defensible, but the version defended here needs additional evidence or a more modest formulation.

major comments (3)
  1. [§2, Figures 1–2] The corpus evidence is not fit for the claim that 'authors and journalists are increasingly comfortable ascribing creativity to generative AI.' The bigram 'AI creates' is ambiguous: it may simply mean 'AI produces' without any creativity attribution, and it has no controls (e.g., 'AI writes', 'AI generates', 'AI produces') or baselines for general AI-related discourse. The authors acknowledge in footnote 15 that some uses may be metaphorical, critical, or ironic, but they do not test this possibility; they simply assert it is unlikely. Since this is the first of the two reasons for abandoning the IAC, the claim that ordinary linguistic practice is 'undergoing a sea change' is not established by these figures.
  2. [§2, producer-identity effect and algorithm aversion] The load-bearing functional claim is that the IAC and the process-first approach 'explicitly endorse, and thereby reinforce' algorithm-aversion bias. The cited studies (Magni et al. 2024; Proksch et al. 2024; Hattori et al. 2024; Haverals and Martin 2025) show that people undervalue AI-labelled outputs, but none shows that exposure to or endorsement of the philosophical IAC causes or amplifies this bias. Alternative explanations—general distrust of non-human producers, the effort heuristic, or statistical priors about AI output quality—are not ruled out. Without evidence that dropping the IAC would reduce the bias, the main functional justification for the NSD collapses. A concrete test would compare evaluation under IAC-priming versus product-first framing, or test whether individuals who explicitly endorse the IAC show stronger algorithm aversion.
  3. [§3, NSD] The consistency criterion is under-specified in a way that affects its ability to exclude accidental sources. The paper says consistency is 'not a mere matter of statistical frequency, but of having context-relative grounds for expecting further outputs,' but it does not state what kind of grounds these are—modal, causal, or purely predictive. Consider a system that accidentally produces a run of novel and valuable outputs (e.g., a malfunctioning random generator that happens to produce good results five times). Does it 'can consistently generate' such outputs? The definition needs a principled account of when a productive source counts as reliable, and how 'system' is individuated (base model alone, model plus prompt, model plus user). This matters because the NSD's functional advantage over the IAC is supposed to be that it blocks wasteful investment in accidental sources.
minor comments (4)
  1. [§3, core social function] The identification of the 'core social function' of creativity talk is stipulated rather than argued. Since the whole conceptual-engineering argument depends on this normative premise, a brief justification of why this function takes priority over other functions (e.g., individual praise, responsibility attribution, aesthetic expression) would strengthen the paper. The local-contexts section helps, but the priority claim needs more support.
  2. [References] There are several reference inconsistencies: Dutton is cited as (2009) in the text but the bibliography gives 2003; 'Proksch 2024' and 'Proksch et al. 2024' are used interchangeably; 'Horn et al. 2009' has only two authors in the reference list; the Dreksler reference contains a stray 'Ber2'; and 'Carrol and Wheaton' is a typo for 'Carroll and Wheaton.'
  3. [Figure 2] Figure 2 reports absolute frequencies, while Figure 1 reports percentages of all bigrams. Because the NOW corpus changes in size over time, absolute counts are not directly comparable across years. Normalizing to a per-million-word rate would put the two figures on more equal footing and strengthen the interpretation.
  4. [§2, potential anthropomorphism] The paper mentions in footnote 15 that people may project intentional agency onto AI, citing Dreksler et al. (2025), but it does not integrate this possibility into the main argument. If many 'AI creates' uses are driven by implicit anthropomorphism, then those uses do not straightforwardly support dropping the IAC; they might instead support rethinking how the IAC is applied. This tension should be addressed explicitly.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a normative conceptual-engineering proposal, not a derivation that reduces to its own inputs.

full rationale

The paper contains no mathematical derivation, fitted parameters, or empirical prediction that is forced by construction. Its central move is to propose a New Standard Definition (NSD) that replaces the Intentional Agency Condition with a consistency requirement. The closest structural analogue to circularity is that the proposed consistency criterion mirrors the 'core social function' that the paper itself stipulates: 'we suggest that the core social function of the language of creativity is to identify, endorse, and encourage the generation of novel and valuable artefacts' (Section 2), and later 'we should be looking to use these concepts to identify consistent sources of novel and valuable products' (Section 3). But this is an openly normative premise in a conceptual-engineering argument, not an empirical result being presented as derived from data. The paper does not fit a parameter and then 'predict' the same quantity; the corpus analyses are used as evidence of linguistic drift, not as outputs of a model. The claim that the IAC 'explicitly endorse[s], and thereby reinforce[s]' algorithm-aversion bias is an asserted causal hypothesis with weak evidential support, but unsupported causation is a correctness/evidence problem, not circularity. The only self-citation (Pearson 2021) supports a peripheral point about malevolent creativity and is not load-bearing. The paper also acknowledges limitations of its own corpus evidence ('even if such attributions are sometimes metaphorical or unreflective'; 'some of these ascriptions ... could be critical or ironic'), further indicating that the evidence is being presented as defeasible rather than as a self-fulfilling derivation. No circular step meeting the quoted-reduction standard is present.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

No numerical fit parameters; the paper's load-bearing premises are normative/functional assumptions and an empirical inference from bias studies. It introduces no new physical or theoretical entities.

assumptions (5)
  • domain assumption The core social function of the language of creativity is to identify, endorse, and encourage reliable sources of novel and valuable products.
    Basis of the functional argument; drawn from praise/resource-allocation literature but not empirically established; see Section 1.
  • domain assumption Generative AI systems lack intentional agency and consciousness.
    Section 2, footnote 9: the authors take consciousness to be an essential component of intentional agency; this premise excludes hybrid or co-creativity arguments that might bypass the IAC.
  • domain assumption The process-first/IAC heuristic causes the algorithm-aversion bias documented in the cited empirical studies.
    Inferred from producer-identity and effort-heuristic studies; no causal link is demonstrated in the paper; Section 2.
  • ad hoc to paper Conceptual engineering should revise concepts to maximize their functional value.
    Methodological stance adopted from Thomasson and Queloz; a normative premise not defended within this paper; Introduction and Section 1.
  • domain assumption The standard definition of creativity (novelty + value) is an acceptable baseline.
    Accepted from Runco and Jaeger 2012; the paper's NSD modifies only the condition set, not the novelty/value core.

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Cite this review

Pith. "Pith review of Creativity Reconsidered: Generative AI and the Problem of Intentional Agency." pith.science (2026). https://pith.science/paper/IWTGFMBN

@misc{pith2026260115797,
  author       = {Pith},
  title        = {Pith review of: Creativity Reconsidered: Generative AI and the Problem of Intentional Agency},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IWTGFMBN}},
  note         = {Machine review of arXiv:2601.15797}
}
read the original abstract

Many theorists maintain that conscious intentional agency is a necessary condition of creativity. We argue that this requirement, which we call the Intentional Agency Condition (IAC), should be abandoned. We motivate this by highlighting the problems this criterion encounters in the face of recent advances in generative AI, which is ostensibly creative despite being incapable of intentional agency. We present two corpus analyses to illustrate the rapidly increasing tendency of people to predicate creativity to generative AI. In response to this predicament, theorists of creativity have proposed a range of conflicting solutions, which we critically evaluate. We find that none of these satisfyingly resolves the initial predicament, and we therefore propose a novel approach. Our claim is that ascriptions of creativity are dependent on what we call creative ability. This solution explains why intentional agency is important for judgements of creativity, without being a necessary condition. Our approach thereby accommodates AI creativity without dismissing the intuition that perceived intentions are of key importance for ascriptions of creativity.

Figures

Figures reproduced from arXiv: 2601.15797 by the authors.

Figure 1
Figure 1. Google Ngram Viewer trend data for {AI creates, scientist creates, painter creates}, 1900–2022 inclusive. The score returned by Google Ngram Viewer is expressed as a percentage of all available bigrams in Google’s corpus, up to and including the year 2022 (at time of writing). Adapted from raw data by Google (2024); search conducted 12 Nov 2025 at URL = https://books.google.com/ngrams/graph?content=AI+creates%2C+sci… view at source ↗
Figure 2
Figure 2. News on the Web trend data for {AI creates, scientist creates, painter creates}, 2010–2022 (inclusive). Adapted from raw data by Davies (2016–); search conducted 12 Nov 2025 at URL = https://www.english-corpora.org/now/. To standardize the cut-off date with our previous investigation of Google Ngram Viewer (see [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 2
Figure 2. 24 This goes against those who say that if we cannot tell the creative outputs of machines apart from those of humans, then we will have no real basis for distinguishing between the two types of creativity (e.g., Chen 2018) [PITH_FULL_IMAGE:figures/full_fig_p018_2.png] view at source ↗

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Reference graph

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Pith tools

Reviewed August 3, 2026 · model on record in the stance chip above.