{"id":"b9aafc20-17cc-4f1c-b4cb-c93a92cbf123","arxiv_id":"2504.15469","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"The paper defines aspirational affordances and aspirational harms, arguing that AI can reduce the interpretive resources people use to imagine alternative futures.","lead":"AI systems increasingly generate the stories, images, and advice that shape what people imagine for themselves. This paper introduces terms for analyzing that influence, and argues that AI can harm people by narrowing the futures they see as possible, independent of stereotyping or unfair allocation.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Distinctness of aspirational harm relies on a narrowed definition of representational harm that conflicts with the paper's own earlier definition in Section 4.1, leaving the central claim unstable.","rationale":"The reader identified the distinctness assumption as the weakest point, specifically the reliance on a stipulated narrow definition of representational harm. My concern sharpens this: the paper's Section 4.1 contains two incompatible definitions, and the distinctness argument requires the narrow one while the paper's own introductory characterization of representational harm is broad enough to cover its own aspirational-harm examples. This is an internal inconsistency, not merely a contestable stipulation, making the central claim more fragile than the reader's 'partial' framing suggests. Both the reader and I agree that the central claim is conditional; my concern provides a more concrete textual basis. The suggested test—re-running the examples through the paper's own first definition—would settle the issue analytically. If the collapse is confirmed, the paper's contribution reduces to a useful subtype of representational harm rather than a distinct category, though the conceptual framework for aspational affordances could still stand. I therefore agree with CONDITIONAL rather than moving to REJECT, since the authors could plausibly repair the argument by explicitly justifying the narrow definition or by revising the initial characterization. The paper's empirical thinness, while noted, is secondary to this definitional instability for the central claim.","tokens_in":17333,"tokens_out":4007,"duration_ms":39109,"concrete_test":"Re-evaluate the paper's three case studies (Sections 4.2, 4.3, 4.4) against the first definition of representational harm in Section 4.1 (denigrating or stereotypical representations reinforcing social subordination or cultural erasure along identity lines). For each case, determine whether the described harm satisfies that definition. If all three do, aspirational harm is not a distinct category under the paper's own starting characterization; if only some do, the distinctness claim must be restricted to the remaining cases. This analytical substitution directly tests which definition the central argument actually requires.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"Section 4.1 opens by defining representational harms as 'denigrating or stereotypical representations of certain groups, reinforcing or amplifying patterns of social subordination and cultural erasure along identity lines.' Later, the paper narrows this to harms that occur 'in virtue of the fact that certain groups are portrayed in ways that do not reflect their relative standing or worth,' and uses this narrow sense to argue that aspirational harms (e.g., women depicted in nurturing roles) are not representational. But the earlier, broader definition already covers the flagship aspirational-harm example: systematic depiction of women (and not men) in nurturing roles is a stereotypical representation that reinforces social subordination and culturally erases women in non-nurturing roles. The argument for distinctness therefore depends on silently replacing the initial definition with a narrower one. If the initial definition governs, aspirational harm collapses into representational harm and the paper's central contribution fails; the paper never justifies why the narrow definition should be preferred. This is an internal tension, not just a stipulated boundary, because both characterizations appear in the same section and the paper relies on each at different points.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper introduces 'aspirational affordance' to describe how culturally shared interpretive resources shape practical imagination, and 'aspirational harm' to describe AI-enabled harms that arise when these affordances distort or diminish the interpretive resources available for imagining possible selves and alternative futures. The authors argue that AI's influence on aspirational affordances is distinctive because it is more potent but less public than traditional media, ecological rather than incremental, and oligopolistic in its concentration. They contend that aspirational harm is a distinct category from representational and allocative harms, and illustrate the claim with three case studies: AI-mediated career guidance that narrows professional selves, DALL-E's elaboration of futuristic images of Iranian schoolgirls, and AI's reinforcement of status-quo narratives in search and writing tools. The paper concludes with calls for further normative and empirical work.","tokens_in":17335,"tokens_out":9508,"duration_ms":76682,"significance":"If the conceptual framework withstands scrutiny, it makes a useful contribution to AI harms discourse by shifting attention from how AI represents the world to how it constrains the imaginative space for individuals and groups. The paper is generally careful: it cites relevant empirical work (possible selves, system justification theory, hermeneutical injustice), explicitly flags the informal nature of its DALL-E probe, and acknowledges that broader normative characterization is left for future work. However, the central claim of distinctness rests on a stipulated and internally inconsistent narrowing of 'representational harm' in Section 4.1, and the second case study's evidence base is too thin to support the empirical generalizations drawn. These issues are fixable, but they are load-bearing for the paper's main contribution.","major_comments":[{"comment":"The paper gives two different characterizations of representational harm. The first defines them as 'denigrating or stereotypical representations of certain groups, reinforcing or amplifying patterns of social subordination and cultural erasure along identity lines.' The second narrows them to harms that occur 'in virtue of the fact that certain groups are portrayed in ways that do not reflect their relative standing or worth.' The argument that aspirational harm is distinct from representational harm runs through the narrow characterization: the 'woman as nurturer' example is said not to be a representational harm because it does not misrepresent women's relative standing or worth. But under the initial, broader definition, that example is a stereotypical representation that reinforces social subordination (traditional gender roles) and contributes to cultural erasure of women in non-nurturing roles. The paper does not acknowledge or justify this shift. This is load-bearing: if the initial definition governs, the flagship example collapses into representational harm and the distinctness claim is substantially weakened. The authors should either adopt the narrow definition from the outset and defend it against the broad definition used in the cited literature, or reformulate the distinctness argument so it does not depend on this particular delimitation.","section":"Section 4.1, paragraphs 1 and 6"},{"comment":"The paper presents the DALL-E probe as revealing a 'striking pattern' in which AI-generated futures are technology-centric but politically conservative, and uses this to illustrate aspirational harm. However, the probe consists of an unspecified number of generations (only two are shown), no baseline or comparison condition, no systematic coding protocol, no model version or date, and no information about prompt variations or sampling. The authors explicitly call the test 'informal' and disclaim it as a systematic audit, which is commendable. Yet the surrounding text makes claims such as 'across generated images, technological change functions as the dominant symbol of progress' and 'such a pattern by default renders... invisible.' These are empirical generalizations that the reported evidence cannot support. If the case study is meant only as an illustration, the claims should be framed more cautiously; if it is meant to support a general claim about AI's systematic behavior, the evidence is insufficient. The same applies to Table 1's 'system representation' expansions, whose provenance and representativeness are not documented.","section":"Section 4.3, Figure 1 and Table 1"}],"minor_comments":[{"comment":"The phrase 'a particularly useful for grounding evaluations' is missing a noun; it should read 'a particularly useful tool for grounding evaluations.'","section":"Abstract and Section 1"},{"comment":"'in particular exercises practical imagination' appears to be a typo for 'in particular exercises of practical imagination.'","section":"Section 2, paragraph 1"},{"comment":"The sentence 'the seamless design through which the AI outputs is communicated to users' is grammatically incorrect; consider 'through which AI outputs are communicated.'","section":"Section 3.1, paragraph 3"},{"comment":"'this temptations' should be 'this temptation.'","section":"Section 4.1, paragraph 6"},{"comment":"The caption should state the DALL-E model version, the date of generation, and the total number of images generated, and clarify how the two shown images were selected from the full set; as written, the informal description is not reproducible.","section":"Section 4.3, Figure 1 caption"},{"comment":"It would be helpful to clarify whether the 'system representation' text is quoted verbatim from the model's internal expansion or is the authors' paraphrase; as written, the provenance is unclear.","section":"Section 4.3, Table 1"}],"recommendation":"major_revision","confidential_remarks":"The paper's contribution is primarily conceptual, and its fit with cs.CY is appropriate. The main risk is that the distinctness argument, if not repaired, will be seen as stipulative rather than argued. I recommend major revision rather than rejection because the framework can likely be made coherent with focused revision."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Here's my take. This paper gives AI ethics a genuinely useful organizing concept: aspirational affordances, and the related notion of aspirational harm. It connects Gibsonian affordances, possible selves, and hermeneutical injustice into a single lens for evaluating how AI-generated representations shape what people can imagine for themselves. That is a real contribution, and the three reasons for special scrutiny—potent but private, ecological, and oligopolistic—are plausible and well supported by the literature. The citation practice is solid; the authors engage recent work and don't just cite themselves.\n\nThe stress-test note is right. In Section 4.1 the paper defines representational harms broadly: denigrating or stereotypical representations that reinforce subordination and cultural erasure. A few paragraphs later it narrows the definition to portrayals that fail to reflect groups' relative standing or worth, and uses that narrow version to argue that positive but restrictive stereotypes (women only in nurturing roles) are not representational harms. But that example already fits the broad definition: it's a stereotypical representation that reinforces subordination and erases women in non-nurturing roles. The paper never explains why the narrow definition is the correct one. This is an internal tension, not just a boundary dispute. It means the paper's central claim—that aspirational harm is a distinct category warranting separate attention—is not yet established. The fix is straightforward: either adopt one definition consistently and show where aspirational harm falls, or reframe the contribution as a subtype of representational harm.\n\nThe DALL-E case study is explicitly informal—two samples, no systematic protocol. That's fine for illustration, but it can't carry much empirical weight. The paper is transparent about this, so I don't count it as a serious flaw.\n\nOverall, the framework is worth having and the flaw is fixable in revision. This deserves a serious referee, not a desk reject. I'd send it to reviewers and expect a conditional acceptance if the authors resolve the definitional tension. I'd cite the paper in my own work, probably with a caveat about the distinctness claim.","headline":"Useful new framework for AI's effect on aspiration, but the claim of a distinct harm category rests on a definitional shift the paper doesn't justify.","tokens_in":18032,"tokens_out":3912,"would_cite":true,"duration_ms":33639,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"AI systems can inflict a distinct kind of harm: narrowing the shared interpretive resources through which people imagine what they could become.","keywords":["aspirational harm","aspirational affordances","practical imagination","generative AI","representational harm","allocative harm","hermeneutical injustice","AI ethics"],"falsifier":"A direct test would be a systematic audit of image generators using the paper's own query about Iranian girls standing in front of their school in 2040; if political and social transformation appeared as frequently as technological change in the generated images, the illustrative case would lose its empirical grounding. More generally, a study showing that people exposed to AI-generated positive-but-narrow archetypes show no measurable reduction in the range of futures they enumerate, consider possible, or rate as attainable—and that collective narratives remain as diverse as before—would undermine the claim that these outputs constitute a distinct harm.","tokens_in":16945,"feed_emoji":"💭","tokens_out":5682,"duration_ms":52363,"temperature":0.7,"pith_summary":"The paper argues that AI systems can harm people not only by misrepresenting groups or misallocating resources, but by shrinking the interpretive resources people use to imagine their own possible futures. It names this injury aspirational harm and defines it as occurring when AI-enabled aspirational affordances distort or diminish shared concepts, images, and narratives so that individuals or groups lose the capacity to envision practically relevant alternatives. The paper introduces the concept of aspirational affordance, adapted from ecological psychology, to describe how culturally shared interpretive resources open up or close down possibilities for practical imagination. It gives three reasons AI deserves special scrutiny: its influence is more potent and less public than older media, it is ecological rather than additive, and it is concentrated in a few corporate-controlled systems. Three case studies—career-advice AI, AI-generated images of Iranian schoolgirls in 2040, and AI shaping political attitudes—illustrate why aspirational harms should be tracked separately from representational and allocative harms.","feed_headline":"AI can shrink the futures people imagine—a distinct harm","feed_subtitle":"Beyond bias and unfairness, AI can narrow the shared vocabulary of possible futures.","key_machinery":"The load-bearing concept is aspirational affordance, defined as a culturally shared interpretive resource—a concept, image, narrative, symbol, or other representational modality—that enables, enhances, constrains, or otherwise shapes an individual's practical imagination. Borrowing from ecological psychology's affordances, the concept emphasizes relationality: whether an AI output affords a particular aspiration depends on the user's skills, expectations, and social context, and perception of an affordance is distinct from acting on it. This machinery carries the argument by shifting evaluation away from static properties of AI outputs and toward the dynamic relationship between outputs, users, and the cultural landscape, thereby grounding claims about which harms are distinct and why they warrant separate attention.","core_discovery":"The central discovery is a previously unnamed category of AI harm: aspirational harm. In the context of AI, this harm arises when AI-enabled aspirational affordances distort, delimit, or diminish available interpretive resources in ways that undermine a group's ability to imagine possible selves, alternative futures, or practical pathways for realizing them. The paper argues this is a distinct agential harm, not a species of representational or allocative harm, because it concerns the imaginative and aspirational capacities of agents rather than the accuracy of portrayals or the fairness of resource distributions. A group can suffer aspirational harm even when all representations properly reflect its members' relative standing and worth, as when AI systems depict women exclusively in nurturing roles. By structuring the interpretive landscape available for re-imagining and changing the world, AI-enabled aspirational affordances constrain what individuals and communities can even conceive as a future they might pursue.","pith_inferences":["The paper leaves implicit a testable prediction: exposure to AI-generated narrow archetypes should measurably reduce the range of careers, selves, or futures that people list, rate as attainable, or spontaneously generate; this could be studied with possible-selves inventories or open-ended future-elaboration tasks.","Because the mechanism is relational, interventions aimed at expanding interpretive resources—counter-narratives, diverse exemplars, participatory content creation—could be evaluated by their effect on the range and richness of imagined futures rather than by representational parity alone.","The same conceptual machinery could be extended beyond AI to human-authored cultural production, offering a unified account of how any cultural ecosystem entrenches or expands the imaginable; the paper restricts its focus to AI but does not argue the phenomenon is AI-exclusive.","The boundary between aspirational and representational harm is the paper's most fragile point: if representational harm is defined broadly enough to include any portrayal that constrains what a group can imagine being, the new category collapses into the old one, so the distinctness claim rests on defending the narrower definition of representational harm."],"forward_implications":["If aspirational harm is distinct, then bias-mitigation metrics that track only representational parity are insufficient for evaluating AI's social impact; systems can score well on fairness and still harm by narrowing imagined selves.","AI systems that consistently associate leadership with male pronouns produce not only representational and allocative harms but also a narrowing of the interpretive resources available for imagining women in high-status professions.","Fairness interventions that add positive but narrow archetypes—such as the resilient woman in STEM—can inadvertently erase alternative forms of professional identity and leadership.","AI-elaborated futures that foreground technological change while leaving political arrangements intact can render the aspirations of activists and marginalized communities invisible and erode the perceived plausibility of social transformation.","Because AI influence is concentrated and ecological, even arbitrary patterns in a few systems' outputs risk becoming entrenched and treated as normative, making aspirational harms more systematic than those of heterogeneous traditional media."],"supporting_citations":[{"why":"Provides the ecological notion of affordances that the paper adapts to culturally shared interpretive resources.","marker":"(Gibson 1977, 2014)"},{"why":"Supplies the account of hermeneutical resources and epistemic injustice from which aspirational harm's focus on interpretive resources is drawn.","marker":"(Fricker 2007)"},{"why":"Frames representational harms of AI and notes how seamless AI design encourages anthropomorphism and perceived objectivity, supporting the potency claim.","marker":"(Chien and Danks 2024)"},{"why":"Demonstrates that statistical disparities in AI portrayals do not reliably predict downstream harms, motivating affordance-based sociotechnical evaluation.","marker":"(Wang et al. 2022)"},{"why":"Introduces the allocative-versus-representational harm distinction that the paper argues aspirational harm extends beyond.","marker":"(Barocas et al. 2017)"},{"why":"Provides the broader taxonomy of sociotechnical harms against which aspirational harm is positioned.","marker":"(Shelby et al. 2023)"},{"why":"Supplies the 'amazing bounce-backable woman' ideal used in the career-advice case study to illustrate a restrictive archetype.","marker":"(Gill and Orgad 2018)"},{"why":"Supplies the system justification theory used in the third case study to link status-quo narratives to narrowed aspirations.","marker":"(Jost and Hunyady 2005)"}],"fun_headline_variants":["AI narrows the range of futures we can imagine","Aspirational harm: AI's quieter theft of possible selves","AI shrinks our imaginative horizon—a distinct harm","When AI limits what you believe you can become","AI's hidden cost: fewer possible futures, not just unfair ones"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The new category only holds if representational harm means portraying groups in a way that fails to reflect their relative standing or worth; if the term is broadened to include any portrayal that narrows what people can imagine being, aspirational harm is just a version of representational harm and the paper's central contribution fails.","fun_headline_variants_meta":{"raw":{"variants":["AI narrows the range of futures we can imagine","Aspirational harm: AI's quieter theft of possible selves","AI shrinks our imaginative horizon—a distinct harm","When AI limits what you believe you can become","AI's hidden cost: fewer possible futures, not just unfair ones"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.001429,"raw_usage":{"total_tokens":5785,"prompt_tokens":987,"completion_tokens":4798,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":603,"completion_tokens_details":{"reasoning_tokens":4718}},"tokens_in":603,"tokens_out":4798,"duration_ms":30974,"temperature":1.0,"reasoning_tokens":4718,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-16T11:26:08.382872+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"A direct test would be a systematic audit of image generators using the paper's own query about Iranian girls standing in front of their school in 2040; if political and social transformation appeared as frequently as technological change in the generated images, the illustrative case would lose its empirical grounding. More generally, a study showing that people exposed to AI-generated positive-but-narrow archetypes show no measurable reduction in the range of futures they enumerate, consider possible, or rate as attainable—and that collective narratives remain as diverse as before—would undermine the claim that these outputs constitute a distinct harm.","supporting_citations":[],"review_version":1}