Remi Denton
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Towards Equitable Community-Industry Collaborations: Understanding the Experiences of Nonprofits' Collaborations with Tech Companies
Sheena Erete
Eric Corbett
Natasha Smith-Walker
Jay L Cunningham
Erin Gatz
Tina Park
Tam Perry
Lauren Wilcox
Proceedings of the ACM on Human-Computer Interaction (2025)
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Community-based partnerships are essential to creating inclusive and equitable technologies and design practices. Though recent scholarship in HCI focuses on equitable design practices, there is less focus on understanding the experiences of community-based nonprofit organizations (CBOs) when partnering with technology companies. In this paper, we focus on understanding the perspectives of CBOs by answering the following research question: What are the experiences of CBOs that have collaborated with technology companies? Through a series of design workshops with 18 participants who work at community-based nonprofits that have collaborated with technology firms, we identified four elements of community-industry collaborations that collectively shape the overall experience: divergences in cultural and organizational norms, ''setting the table,'' project relationship dynamics, and affective qualities. We conclude by discussing the power structures that impact community-industry collaboration and suggest reflective practices to guide equitable collaborations between CBOs and tech companies.
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Amplifying Trans and Nonbinary Voices: A Community-Centred Harm Taxonomy for LLMs
Eddie Ungless
Sunipa Dev
Beka Gulotta
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (2025)
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We explore large language model (LLM) responses that may negatively impact the transgender and nonbinary (TGNB) community and introduce the Transing Transformers Toolkit, T3, which provides resources for identifying such harmful response behaviors. The heart of T3 is a community-centred taxonomy of harms, developed in collaboration with the TGNB community, which we complement with, amongst other guidance, suggested heuristics for evaluation. To develop the taxonomy, we adopted a multi-method approach that included surveys and focus groups with community experts. The contribution highlights the importance of community-centred approaches in mitigating harm, and outlines pathways for LLM developers to improve how their models handle TGNB-related topics.
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The field of computer vision is now a multi-billion dollar enterprise, with its use in surveillance applications driving this large market share. In the last six years, computer vision researchers have started to discuss the risks and harms of some of these systems, mostly using the lens of fairness introduced in the machine learning literature to perform this analysis. While this lens is useful to uncover and mitigate a narrow segment of the harms that can be enacted through computer vision systems, it is only one of the toolkits that researchers have available to uncover and mitigate the harms of the systems they build.
In this monograph, we discuss a wide range of risks and harms that can be enacted through the development and deployment of computer vision systems. We also discuss some existing technical approaches to mitigating these harms, as well as the shortcomings of these mitigation strategies. Then, we introduce computer vision researchers to harm mitigation strategies proposed by journalists, human rights activists, individuals harmed by computer vision systems, and researchers in disciplines ranging from sociology to physics. We conclude the monograph by listing principles that researchers can follow to build what we call community-rooted computer vision tools in the public interest, and give examples of such research directions. We hope that this monograph can serve as a starting point for researchers exploring the harms of current computer vision systems and attempting to steer the field into community-rooted work.
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Dialogue with the machine and dialogue with the art world: evaluating generative AI for culturally-situated creativity
Aroussiak Gabriellan
Farbod Mehr
Huma Gupta
Pamela Karimi
NeurIPS Creative AI Track (2024)
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This paper proposes dialogue as a method for evaluating generative AI tools for culturally-situated creative practice, that recognizes the socially situated nature of art. Drawing on sociologist Howard Becker's concept of Art Worlds, this method expands the scope of traditional AI and creativity evaluations beyond benchmarks, user studies with crowd-workers, or focus groups conducted with artists. Our method involves two mutually informed dialogues: 1) 'dialogues with art worlds' placing artists in conversation with experts such as art historians, curators, and archivists, and 2)'dialogues with the machine,' facilitated through structured artist- and critic-led experimentation with state-of-the-art generative AI tools. We demonstrate the value of this method through a case study with artists and experts steeped in non-western art worlds, specifically the Persian Gulf. We trace how these dialogues help create culturally rich and situated forms of evaluation for representational possibilities of generative AI that mimic the reception of generative artwork in the broader art ecosystem. Putting artists in conversation with commentators also allow artists to shift their use of the tools to respond to their cultural and creative context. Our study can provide generative AI researchers an understanding of the complex dynamics of technology, human creativity and the socio-politics of art worlds, to build more inclusive machines for diverse art worlds.
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ViSAGe: A Global-Scale Analysis of Visual Stereotypes in Text-to-Image Generation
Akshita Jha
Sarah Laszlo
Chandan Reddy
Sunipa Dev
ACL (2024)
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Recent studies have highlighted the issue of varying degrees of stereotypical depictions for different identity group. However, these existing approaches have several key limitations, including a noticeable lack of coverage of identity groups in their evaluation, and the range of their associated stereotypes. Additionally, these studies often lack a critical distinction between inherently visual stereotypes, such as `brown' or `sombrero', and culturally influenced stereotypes like `kind' or `intelligent'. In this work, we address these limitations by grounding our evaluation of regional, geo-cultural stereotypes in the generated images from Text-to-Image models by leveraging existing textual resources. We employ existing stereotype benchmarks to evaluate stereotypes and focus exclusively on the identification of visual stereotypes within the generated images spanning 135 identity groups. We also compute the offensiveness across identity groups, and check the feasibility of identifying stereotypes automatically. Further, through a detailed case study and quantitative analysis, we reveal how the default representations of all identity groups have a more stereotypical appearance, and for historically marginalized groups, how the images across different attributes are visually more similar than other groups, even when explicitly prompted otherwise.
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“They only care to show us the wheelchair”: disability representation in text-to-image AI models
Avery Mack
CHI Conference on Human-Computer Interaction (2024)
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This paper reports on disability representation in images output from text-to-image (T2I) generative AI systems. Through eight focus groups with 25 people
with disabilities, we found that models repeatedly presented reductive archetypes for different disabilities. Often these representations reflected broader
societal stereotypes and biases, which our participants were concerned to see reproduced through T2I. Our participants discussed further challenges with
using these models including the current reliance on prompt engineering to reach satisfactorily diverse results. Finally, they offered suggestions for
how to improve disability representation with solutions like showing multiple, heterogeneous images for a single prompt and including the prompt with images
generated. Our discussion reflects on tensions and tradeoffs we found among the diverse perspectives shared to inform future research on representation-oriented
generative AI system evaluation metrics and development processes.
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Towards Globally Responsible Generative AI Benchmarks
ICLR Workshop : Practical ML for Developing Countries Workshop (2023)
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As generative AI globalizes, there is an opportunity to reorient our nascent development frameworks and evaluative practices towards a global context. This paper uses lessons from a community-centered study on the failure modes of text to Image models in the South Asian context, to give suggestions on how the AI/ML community can develop culturally and contextually situated benchmarks. We present three forms of mitigations for culturally situated- evaluations: 1) diversifying our diversity measures 2) participatory prompt dataset curation 2) multi-tiered evaluations structures for community engagement. Through these mitigations we present concrete methods to make our evaluation processes more holistic and human-centered while also engaging with demands of deployment at global scale.
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AI’s Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asia
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, Association for Computing Machinery, 506–517
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This paper presents a community-centered study of cultural limitations of text-to-image (T2I) models in the South Asian context. We theorize these failures using scholarship on dominant media regimes of representations and locate them within participants’ reporting of their existing social marginalizations. We thus show how generative AI can reproduce an outsiders gaze for viewing South Asian cultures, shaped by global and regional power inequities. By centering communities as experts and soliciting their perspectives on T2I limitations, our study adds rich nuance into existing evaluative frameworks and deepens our understanding of the culturally-specific ways AI technologies can fail in non-Western and Global South settings. We distill lessons for responsible development of T2I models, recommending concrete pathways forward that can allow for recognition of structural inequalities.
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"I wouldn’t say offensive but...": Disability-Centered Perspectives on Large Language Models
Vinitha Gadiraju
Sunipa Dev
Alex Taylor
Robin Brewer
Proceedings of FAccT 2023 (2023) (to appear)
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Large language models (LLMs) trained on real-world data can inadvertently reflect harmful societal biases, particularly toward historically marginalized communities. While previous work has primarily focused on harms related to age and race, emerging research has shown that biases toward disabled communities exist. This study extends prior work exploring the existence of harms by identifying categories of LLM-perpetuated harms toward the disability community. We conducted 19 focus groups, during which 56 participants with disabilities probed a dialog model about disability and discussed and annotated its responses. Participants rarely characterized model outputs as blatantly offensive or toxic. Instead, participants used nuanced language to detail how the dialog model mirrored subtle yet harmful stereotypes they encountered in their lives and dominant media, e.g., inspiration porn and able-bodied saviors. Participants often implicated training data as a cause for these stereotypes and recommended training the model on diverse identities from disability-positive resources. Our discussion further explores representative data strategies to mitigate harm related to different communities through annotation co-design with ML researchers and developers.
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CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation
Ian Kivlichan
Dylan Baker
Razvan Adrian Amironesei
Association for Computing Machinery, 2342–2351
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Human annotated data plays a crucial role in machine learning (ML) research and development. However, the ethical considerations around the processes and decisions that go into dataset annotation have not received nearly enough attention. In this paper, we survey an array of literature that provides insights into ethical considerations around crowdsourced dataset annotation. We synthesize these insights, and lay out the challenges in this space along two layers: (1) who the annotator is, and how the annotators' lived experiences can impact their annotations, and (2) the relationship between the annotators and the crowdsourcing platforms, and what that relationship affords them. Finally, we introduce a novel framework, CrowdWorkSheets, for dataset developers to facilitate transparent documentation of key decisions points at various stages of the data annotation pipeline: task formulation, selection of annotators, platform and infrastructure choices, dataset analysis and evaluation, and dataset release and maintenance.
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