Pith. sign in

REVIEW 2 major objections 6 minor 67 references

Aspirational Affordances of AI

T0 review · 2 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read AI systems can inflict a distinct kind of harm: narrowing the shared interpretive resources through which people imagine what they could become.

desk verdict 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. read the letter →

arxiv 2504.15469 v1 pith:TOUNQBKM submitted 2025-04-21 cs.CY

Sina Fazelpour , Meica Magnani This is my paper · ORCID
classification cs.CY
keywords aspirationalharmaffordancespracticalimaginationgenerativeAIrepresentationalallocativehermeneuticalinjusticeethics
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 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.

What carries the argument

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.

What would settle it

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.

Watch

Extended reading notes

Core claim

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.

Load-bearing premise

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.

Editorial extensions

If this is right

  • 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.

Reading between the lines

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

  • 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.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 6 minor

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.

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 (2)
  1. [Section 4.1, paragraphs 1 and 6] 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.
  2. [Section 4.3, Figure 1 and Table 1] 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.
minor comments (6)
  1. [Abstract and Section 1] The phrase 'a particularly useful for grounding evaluations' is missing a noun; it should read 'a particularly useful tool for grounding evaluations.'
  2. [Section 2, paragraph 1] 'in particular exercises practical imagination' appears to be a typo for 'in particular exercises of practical imagination.'
  3. [Section 3.1, paragraph 3] The sentence 'the seamless design through which the AI outputs is communicated to users' is grammatically incorrect; consider 'through which AI outputs are communicated.'
  4. [Section 4.1, paragraph 6] 'this temptations' should be 'this temptation.'
  5. [Section 4.3, Figure 1 caption] 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.
  6. [Section 4.3, Table 1] 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.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's conceptual claims rest on definitions, philosophical argument, and external empirical literature, not on fitted inputs or self-citation chains.

full rationale

This is a conceptual and normative philosophy paper rather than an empirical or mathematical derivation. The central contribution is the introduction of the terms 'aspirational affordance' and 'aspirational harm' together with an argument that aspirational harm is distinct from representational and allocative harms. There are no equations, fitted parameters, or predictions whose values are determined by the inputs. The distinctness argument does depend on a stipulated characterization of representational harm as tied to failures to reflect a group's relative standing or worth, and one could contest that characterization; the paper's earlier broader definition of representational harm as covering stereotypical representations that reinforce subordination may indeed be in tension with the later narrowing. That is a substantive objection to the strength or correctness of the distinctness claim, but it is not circularity: the paper does not define aspirational harm as 'the non-representational remainder' and then infer distinctness from that definition. Instead, it offers a substantive philosophical rationale, including an analogy to doxastic wronging, for why representational harm is specifically about standing and worth, and it uses external empirical and philosophical sources (e.g., system justification theory, possible selves research, hermeneutical injustice literature) to support the independent content of aspirational harm. The only self-citation, Fazelpour and Lipton (2020), supports the general point that model-centric evaluations are limited and is not load-bearing for the paper's central conceptual thesis. No load-bearing step reduces to its own inputs, so the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 6 assumptions · 2 invented entities

The central claims rest on background theories of affordances, epistemic injustice, and system justification, and on the paper's own stipulated definitions rather than on measured parameters. No free parameters are present. The two new concepts are not independently validated, and the only direct empirical illustration, the DALL-E test, is informal.

assumptions (6)
  • domain assumption Affordances from ecological psychology provide a valid relational framework for describing how environments shape action possibilities.
    The paper extends Gibsonian affordances to cognitive and aspirational possibilities, presupposing that shared interpretive resources function like affordances for practical imagination (Section 2, opening paragraphs).
  • domain assumption Hermeneutical resources are unequally distributed and their distribution can constitute injustice or harm.
    The definition of aspirational harm draws on Fricker's hermeneutical injustice and the broader literature on epistemic harms (Sections 2 and 4).
  • domain assumption Representational harms are defined as failures to reflect the relative standing or worth of groups.
    The argument that aspirational harms are distinct relies on this stipulated narrow definition of representational harm, defended in Section 4.1 with the analogy to doxastic wronging.
  • domain assumption Personalized, conversational, private AI interactions increase user vulnerability and receptivity to AI-generated content.
    Section 3.1 asserts that these design features 'can heighten user vulnerability' and influence affordance perception and actualization; the claim generalizes across AI systems without a systematic study.
  • domain assumption System justification theory correctly describes a general tendency to defend existing social arrangements.
    Case study 3 (Section 4.4) relies on Jost and Hunyady's system justification theory to argue that AI status-quo narratives reduce perceived plausibility of change.
  • domain assumption Exposure to representations of possibilities affects which futures seem plausible and desirable.
    The paper cites Markus and Nurius, Tversky and Kahneman, and related work for this premise; it is not proved in the paper but is load-bearing for the mechanism of aspirational affordances.
invented entities (2)
  • Aspirational affordance
    purpose: Describes how culturally shared interpretive resources enable, enhance, constrain, or otherwise shape individuals' aspirations and practical imagination.
    A newly coined theoretical construct with no independent measurement or falsifiable prediction in the paper; the authors call for future work to operationalize and measure it.
  • Aspirational harm
    purpose: Names a distinct category of harm when AI-enabled aspirational affordances distort or diminish interpretive resources and undermine the ability to imagine alternative futures.
    A newly coined normative category; the paper provides criteria for identification but no independent empirical handle, and explicitly leaves broader normative characterization to future work.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Aspirational Affordances of AI." pith.science (2026). https://pith.science/paper/TOUNQBKM

@misc{pith2026250415469,
  author       = {Pith},
  title        = {Pith review of: Aspirational Affordances of AI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TOUNQBKM}},
  note         = {Machine review of arXiv:2504.15469}
}
read the original abstract

As artificial intelligence systems increasingly permeate processes of cultural and epistemic production, there are growing concerns about how their outputs may confine individuals and groups to static or restricted narratives about who or what they could be. In this paper, we advance the discourse surrounding these concerns by making three contributions. First, we introduce the concept of aspirational affordance to describe how culturally shared interpretive resources can shape individual cognition, and in particular exercises practical imagination. We show how this concept can ground productive evaluations of the risks of AI-enabled representations and narratives. Second, we provide three reasons for scrutinizing of AI's influence on aspirational affordances: AI's influence is potentially more potent, but less public than traditional sources; AI's influence is not simply incremental, but ecological, transforming the entire landscape of cultural and epistemic practices that traditionally shaped aspirational affordances; and AI's influence is highly concentrated, with a few corporate-controlled systems mediating a growing portion of aspirational possibilities. Third, to advance such a scrutiny, we introduce the concept of aspirational harm, which, in the context of AI systems, arises when AI-enabled aspirational affordances distort or diminish available interpretive resources in ways that undermine individuals' ability to imagine relevant practical possibilities and alternative futures. Through three case studies, we illustrate how aspirational harms extend the existing discourse on AI-inflicted harms beyond representational and allocative harms, warranting separate attention. Through these conceptual resources and analyses, this paper advances understanding of the psychological and societal stakes of AI's role in shaping individual and collective aspirations.

Figures

Figures reproduced from arXiv: 2504.15469 by the authors.

Figure 1
Figure 1. Contrasting current aspirations of Iranian schoolgirls with AI-generated futures. Figures (a) and (b) show screenshots of news coverage of students’ involvement in protests during the “Woman, Life, Freedom” movement, from von Hein (2022) and Graham-Harrison and Foumani (2022), respectively. Figures (c) and (d) show DALL-E generated images in response to variations of the query, “Create an image of Iranian girls stan… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

67 extracted references · 54 canonical work pages

  1. [4]

    https://newlinesmag.com/argument/a-new- iran-has-been-born-a-global-iran/

    A New Iran Has Been Born — A Global Iran. https://newlinesmag.com/argument/a-new- iran-has-been-born-a-global-iran/. Accessed: 2025-02-11. Brian G Bell and Jennifer Clegg

    work page 2025

  2. [12]

    Ruth MJ Byrne

    Picking on the same person: Does algorithmic monoculture lead to outcome homogenization? Advances in Neural Information Processing Systems 35 (2022), 3663–3678. Ruth MJ Byrne

    work page 2022

  3. [13]

    Science 356, 6334 (2017), 183–186

    Semantics derived automatically from language corpora contain human-like biases. Science 356, 6334 (2017), 183–186. John S Carroll

    work page 2017

  4. [16]

    In The 2024 ACM Conference on Fairness, Accountability, and Transparency

    Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation. In The 2024 ACM Conference on Fairness, Accountability, and Transparency . 933–946. Kathleen Creel and Deborah Hellman

    work page 2024

  5. [17]

    Virginia Public Law and Legal Theory Research Paper 2021-13 (2021)

    The algorithmic leviathan: Arbitrariness, fairness, and opportunity in algorithmic decision making systems. Virginia Public Law and Legal Theory Research Paper 2021-13 (2021). Nilanjana Dasgupta and Shaki Asgari

    work page 2021

  6. [19]

    Ethics 128, 4 (2018), 702–727

    On epistemic appropriation. Ethics 128, 4 (2018), 702–727. Jenny L Davis

    work page 2018

  7. [20]

    In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency

    ‘Affordances’ for Machine Learning. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. 324–332. Maria De-Arteaga, Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, and Adam Tauman Kalai

    work page 2023

  8. [21]

    Frontiers: A Journal of Women Studies 33, 1 (2012), 24–47

    A cautionary tale: On limiting epistemic oppression. Frontiers: A Journal of Women Studies 33, 1 (2012), 24–47. Seliem El-Sayed, Canfer Akbulut, Amanda McCroskery, Geoff Keeling, Zachary Kenton, Zaria Jalan, Nahema Marchal, Arianna Manzini, Toby Shevlane, Shannon Vallor, et al

    work page 2012

Show all 67 references
  1. [22]

    arXiv preprint arXiv:2404.15058 (2024)

    A mechanism-based approach to mitigating harms from persuasive generative ai. arXiv preprint arXiv:2404.15058 (2024). Arianna Falbo

    2024 arXiv

  2. [23]

    Hypatia 37, 2 (2022), 343–363

    Hermeneutical injustice: Distortion and conceptual aptness. Hypatia 37, 2 (2022), 343–363. Sina Fazelpour and Zachary C Lipton

    2022

  3. [24]

    20 Aspirational Affordances of AI Miranda Fricker

    From pretraining data to language models to downstream tasks: Tracking the trails of political biases leading to unfair NLP models.arXiv preprint arXiv:2305.08283 (2023). 20 Aspirational Affordances of AI Miranda Fricker

    2023 arXiv

  4. [26]

    Sociological research online 23, 2 (2018), 477–495

    The amazing bounce-backable woman: Resilience and the psychological turn in neoliberalism. Sociological research online 23, 2 (2018), 477–495. Tarleton Gillespie

    2018

  5. [27]

    Big Data & Society 11, 2 (2024), 20539517241252131

    Generative AI and the politics of visibility. Big Data & Society 11, 2 (2024), 20539517241252131. George R Goethals and Scott T Allison

    2024

  6. [28]

    https://www.theguardian.com/world/2022/oct/04/iranian-schoolgirls-take-up-battlecry-as-protests-continue

    Iranian schoolgirls take up battle cry as protests con- tinue. https://www.theguardian.com/world/2022/oct/04/iranian-schoolgirls-take-up-battlecry-as-protests-continue. Accessed: 2025-04-18. Rex Hartson and Pardha S Pyla

    2022

  7. [29]

    arXiv preprint arXiv:2405.10632 (2024)

    Beyond static AI evaluations: advancing human interaction evaluations for LLM harms and risks. arXiv preprint arXiv:2405.10632 (2024). Maurice Jakesch, Advait Bhat, Daniel Buschek, Lior Zalmanson, and Mor Naaman

    2024 arXiv

  8. [30]

    In Proceedings of the 2023 CHI conference on human factors in computing systems

    Co-writing with opinionated language models affects users’ views. In Proceedings of the 2023 CHI conference on human factors in computing systems . 1–15. John T Jost and Orsolya Hunyady

    2023

  9. [33]

    Noûs 58, 3 (2024), 730–754

    On the site of predictive justice. Noûs 58, 3 (2024), 730–754. Paul M Leonardi

    2024

  10. [34]

    MIS quarterly (2011), 147–167

    When flexible routines meet flexible technologies: Affordance, constraint, and the imbrication of human and material agencies. MIS quarterly (2011), 147–167. Shen-yi Liao and Bryce Huebner

    2011

  11. [35]

    Philosophy and Phenomenological Research 103, 1 (2021), 92–113

    Oppressive things. Philosophy and Phenomenological Research 103, 1 (2021), 92–113. Zhen Lin, Shubhendu Trivedi, and Jimeng Sun

    2021

  12. [36]

    arXiv preprint arXiv:2305.19187 (2023)

    Generating with confidence: Uncertainty quantification for black-box large language models. arXiv preprint arXiv:2305.19187 (2023). Catherine A MacKinnon

    2023 arXiv

  13. [39]

    Scientific Reports 14, 1 (2024),

    The potential of generative AI for personalized persuasion at scale. Scientific Reports 14, 1 (2024),

    2024

  14. [40]

    Scientific Reports 14, 1 (2024), 18525

    Establishing the importance of co-creation and self-efficacy in creative collaboration with artificial intelligence. Scientific Reports 14, 1 (2024), 18525. José Medina

    2024

  15. [41]

    Social Epistemology 26, 2 (2012), 201–220

    Hermeneutical injustice and polyphonic contextualism: Social silences and shared hermeneutical responsibilities. Social Epistemology 26, 2 (2012), 201–220. Mostafa Mesgari, Kaveh Mohajeri, and Bijan Azad

    2012

  16. [42]

    ACM SIGMIS Database: the DATABASE for Advances in Information Systems 54, 2 (2023), 29–52

    Affordances and information systems research: taking stock and moving forward. ACM SIGMIS Database: the DATABASE for Advances in Information Systems 54, 2 (2023), 29–52. Lisa Messeri and MJ Crockett

    2023

  17. [43]

    Nature 627, 8002 (2024), 49–58

    Artificial intelligence and illusions of understanding in scientific research. Nature 627, 8002 (2024), 49–58. Laura Mulvey

    2024

  18. [44]

    Media and cultural studies: Keyworks (2006), 342–352

    Visual pleasure and narrative cinema. Media and cultural studies: Keyworks (2006), 342–352. Alva Noë

    2006

  19. [46]

    arXiv preprint arXiv:2501.01056 (2025)

    Risks of Cultural Erasure in Large Language Models. arXiv preprint arXiv:2501.01056 (2025). Rolf Reber, Norbert Schwarz, and Piotr Winkielman

    2025 arXiv

  20. [47]

    Neal J Roese

    Processing fluency and aesthetic pleasure: Is beauty in the perceiver’s processing experience? Personality and social psychology review 8, 4 (2004), 364–382. Neal J Roese

    2004

  21. [49]

    arXiv preprint arXiv:2403.14380 (2024)

    On the conversational persuasiveness of large language models: A randomized controlled trial. arXiv preprint arXiv:2403.14380 (2024). Andrea Scarantino

    2024 arXiv

  22. [51]

    In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society

    Sociotechnical harms of algorithmic systems: Scoping a taxonomy for harm reduction. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society . 723–741. Chenglei Si, Diyi Yang, and Tatsunori Hashimoto

    2023

  23. [52]

    arXiv preprint arXiv:2409.04109 (2024)

    Can llms generate novel research ideas? a large-scale human study with 100+ nlp researchers. arXiv preprint arXiv:2409.04109 (2024). Matthew Noah Smith

    2024 arXiv

  24. [53]

    Philosopher’s Imprint 10, 3 (2010)

    Practical Imagination and its Limits. Philosopher’s Imprint 10, 3 (2010). Thomas A Stoffregen

    2010

  25. [56]

    Topoi (2024), 1–16

    Am I Still Young at 20? Online Bubbles for Epistemic Activism. Topoi (2024), 1–16. 22 Aspirational Affordances of AI Olga Volkoff and Diane M Strong

    2024

  26. [58]

    https://www.dw.com/en/ iran-deaths-of-schoolgirls-further-stoke-public-fury/a-63494532

    Deaths of Iranian schoolgirls further stoke public fury. https://www.dw.com/en/ iran-deaths-of-schoolgirls-further-stoke-public-fury/a-63494532. Accessed: 2025-02-11. Angelina Wang, Solon Barocas, Kristen Laird, and Hanna Wallach

    2025

  27. [59]

    In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency

    Measuring representational harms in image captioning. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency . 324–335. Angelina Wang, Jamie Morgenstern, and John P Dickerson

    2022

  28. [60]

    Nature Machine Intelligence (2025), 1–12

    Large language models that replace human participants can harmfully misportray and flatten identity groups. Nature Machine Intelligence (2025), 1–12. Xuewei Wang, Weiyan Shi, Richard Kim, Yoojung Oh, Sijia Yang, Jingwen Zhang, and Zhou Yu

    2025

  29. [63]

    arXiv preprint arXiv:2310.11986 (2023)

    Sociotechnical safety evaluation of generative ai systems. arXiv preprint arXiv:2310.11986 (2023). David Gray Widder, Sarah West, and Meredith Whittaker

    2023 arXiv

  30. [64]

    Sterling Williams-Ceci, Maurice Jakesch, Advait Bhat, Kowe Kadoma, Lior Zalmanson, Mor Naaman, and Cornell Tech

    (2023). Sterling Williams-Ceci, Maurice Jakesch, Advait Bhat, Kowe Kadoma, Lior Zalmanson, Mor Naaman, and Cornell Tech

    2023

  31. [65]

    PsyArXiv (2024)

    Bias in AI Autocomplete Suggestions Leads to Attitude Shift on Societal Issues. PsyArXiv (2024). Philipp Zech, Simon Haller, Safoura Rezapour Lakani, Barry Ridge, Emre Ugur, and Justus Piater

    2024

  32. [66]

    Adaptive Behavior 25, 5 (2017), 235–271

    Computational models of affordance in robotics: a taxonomy and systematic classification. Adaptive Behavior 25, 5 (2017), 235–271. Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang

    2017

  33. [67]

    arXiv preprint arXiv:1804.06876 (2018)

    Gender bias in coreference resolution: Evaluation and debiasing methods. arXiv preprint arXiv:1804.06876 (2018). 23

    2018 arXiv

  34. [1973]

    Cognitive psychology 5, 2 (1973), 207–232

    Availability: A heuristic for judging frequency and probability. Cognitive psychology 5, 2 (1973), 207–232. Shannon Vallor

    1973

  35. [1977]

    Hilldale, USA 1, 2 (1977), 67–82

    The theory of affordances. Hilldale, USA 1, 2 (1977), 67–82. James J Gibson

    1977

  36. [1978]

    Journal of experimental social psychology 14, 1 (1978), 88–96

    The effect of imagining an event on expectations for the event: An interpretation in terms of the availability heuristic. Journal of experimental social psychology 14, 1 (1978), 88–96. Anthony Chemero

    1978

  37. [1984]

    Journal of experimental psychology: Human perception and performance 10, 5 (1984),

    Perceiving affordances: visual guidance of stair climbing. Journal of experimental psychology: Human perception and performance 10, 5 (1984),

    1984

  38. [1986]

    American psychologist 41, 9 (1986),

    Possible selves. American psychologist 41, 9 (1986),

    1986

  39. [1989]

    Ethics 99, 2 (1989), 314–346

    Sexuality, pornography, and method:" Pleasure under Patriarchy. Ethics 99, 2 (1989), 314–346. Hazel Markus and Paula Nurius

    1989

  40. [1994]

    Journal of personality and Social Psychology 66, 5 (1994),

    The functional basis of counterfactual thinking. Journal of personality and Social Psychology 66, 5 (1994),

    1994

  41. [1998]

    Recuperado de http://www

    Five things we need to know about technological change. Recuperado de http://www. sdca. org/sermons_ mp3/2012/121229_postman_5Things. pdf (1998). Rida Qadri, Aida M Davani, Kevin Robinson, and Vinodkumar Prabhakaran

    1998

  42. [2000]

    Ecological psychology 12, 1 (2000), 1–28

    Affordances and events. Ecological psychology 12, 1 (2000), 1–28. Amos Tversky and Daniel Kahneman

    2000

  43. [2003]

    Philosophy of science 70, 5 (2003), 949–961

    Affordances explained. Philosophy of science 70, 5 (2003), 949–961. Ivy Schweitzer

    2003

  44. [2004]

    Journal of experimental social psychology 40, 5 (2004), 642–658

    Seeing is believing: Exposure to counterstereotypic women leaders and its effect on the malleability of automatic gender stereotyping. Journal of experimental social psychology 40, 5 (2004), 642–658. Emmalon Davis

    2004

  45. [2005]

    Current directions in psychological science 14, 5 (2005), 260–265

    Antecedents and consequences of system-justifying ideologies. Current directions in psychological science 14, 5 (2005), 260–265. Hadas Kotek, Rikker Dockum, and David Sun

    2005

  46. [2006]

    Psychology of women quarterly 30, 3 (2006), 239–251

    Can the media affect us? Social comparison, self-discrepancy, and the thin ideal. Psychology of women quarterly 30, 3 (2006), 239–251. James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al

    2006

  47. [2010]

    Journal of personality and social psychology 98, 1 (2010),

    From what might have been to what must have been: counterfactual thinking creates meaning. Journal of personality and social psychology 98, 1 (2010),

    2010

  48. [2011]

    Beyond fatalism-an empirical exploration of self-efficacy and aspirations failure in Ethiopia. (2011). Eike Bernhard, Jan Recker, and Andrew Burton-Jones

    2011

  49. [2012]

    Ecological Psychology 24, 2 (2012), 159–177

    An ecological approach to reducing the social isolation of people with an intellectual disability. Ecological Psychology 24, 2 (2012), 159–177. Rowan Bell

    2012

  50. [2013]

    MIS quarterly (2013), 819–834

    Critical realism and affordances: Theorizing IT-associated organizational change processes. MIS quarterly (2013), 819–834. Shabnam von Hein. October 19,

    2013

  51. [2016]

    Advances in neural information processing systems 29 (2016)

    Man is to computer programmer as woman is to homemaker? debiasing word embeddings. Advances in neural information processing systems 29 (2016). Rishi Bommasani, Kathleen A Creel, Ananya Kumar, Dan Jurafsky, and Percy S Liang

    2016

  52. [2017]

    Special Interest Group for Computing

    The problem with bias: from allocative to representational harms in machine learning. Special Interest Group for Computing. Information and Society (SIGCIS) 2 (2017). Solon Barocas, Moritz Hardt, and Arvind Narayanan

    2017

  53. [2018]

    In How Shall Affordances Be Refined? Routledge, 181–195

    An outline of a theory of affordances. In How Shall Affordances Be Refined? Routledge, 181–195. Myra Cheng, Maria De-Arteaga, Lester Mackey, and Adam Tauman Kalai. 2023a. Social norm bias: residual harms of fairness-aware algorithms. Data Mining and Knowledge Discovery 37, 5 (...

    2023 arXiv

  54. [2019]

    arXiv preprint arXiv:1906.06725 (2019)

    Persuasion for good: Towards a personalized persuasive dialogue system for social good. arXiv preprint arXiv:1906.06725 (2019). William H Warren

    2019 arXiv

  55. [2021]

    In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency

    On the dangers of stochastic parrots: Can language models be too big?. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency. 610–623. Tanguy Bernard, Stefan Dercon, and Alemayehu Taffesse

    2021

  56. [2022]

    In 2022 IEEE Symposium on Security and Privacy (SP)

    Spinning language models: Risks of propaganda-as-a-service and countermeasures. In 2022 IEEE Symposium on Security and Privacy (SP) . IEEE, 769–786. Solon Barocas, Kate Crawford, Aaron Shapiro, and Hanna Wallach

    2022

  57. [2023]

    Computer Science

    Improving image generation with better captions. Computer Science. https://cdn. openai. com/papers/dall-e-3. pdf 2, 3 (2023),

    2023

  58. [2024]

    arXiv preprint arXiv:2409.11360 (2024)

    Ai suggestions homogenize writing toward western styles and diminish cultural nuances. arXiv preprint arXiv:2409.11360 (2024). Barrett R Anderson, Jash Hemant Shah, and Max Kreminski

    2024 arXiv

  59. [2025]

    The Philosophical Quarterly 75, 2 (2025), 373–395

    ‘Just The Facts’: Thick Concepts and Hermeneutical Misfit. The Philosophical Quarterly 75, 2 (2025), 373–395. Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell

    2025

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.