Rida Qadri
Interdisciplinary researcher on AI and Social Impacts
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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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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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In this paper we interrogate the relationship between two different ways of seeing and knowing urban mobility markets: a top-down algorithmic vision of mobility platforms and a bottom-up experiential vision of drivers. By juxtaposing both perspectives, we argue that these visions do not exist in binaries but in a complex dance of complementarity and competition. The paper dissects two assumptions that the Platform’s View from Above makes in the context of Jakarta: 1) Urban space is an orderable, knowable and abstract container of supply and demand; 2) Drivers are optimizable, interchangeable dots on a map. For each assumption of the platform’s View from Above, we show how the drivers’ experience these assumptions and how their View from Within responds to the gaps in the former. We argue that the Driver's View from Within doesn't only act as a form of resistance to the platform but also as a mode of survival, acquiescence, subversion and encroachment. We thus reflect on the opportunities this entanglement presents for worker agency and any hopes for more ‘worker centered design’ in platform economies. We conclude with thoughts on the power and value of alternative forms of optimizations in our cities.
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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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