Queer In AI: A Case Study in Community-Led Participatory AI

AI, Organizers Of Queer in; Ovalle, Anaelia; Subramonian, Arjun; Singh, Ashwin; Claas, Voelcker,; Sutherland, Danica J.; Locatelli, Davide; Breznik, Eva; Klubička, Filip; Yuan, Hang; Hetvi, J; Zhang, Huan; Shriram, Jaidev; Lehman, Kruno; Soldaini, Luca; Sap, Maarten; Deisenroth, Marc Peter; Pacheco, María Leonor; Ryskina, Maria; Mundt, Martin; Agarwal, Milind; McLean, Nyx; Xu, Ping; Pranav, A; Korpan, Raj; Ray, Ruchira; Mathew, Sarah; Arora, Sarthak; Stachurski, John; Anand, Tanvi; Agrawal, Vishakha; Agnew, William S.; Long, Yanan; Wang, Zijie; Talat, Zeerak; Ghosh, Avijit; Noseworthy, Michael D.; Jha, Sharvani; Baylor, Emi; Joshi, Aditya; Bilenko, Natalia Y.; McNamara, Andrew; Gontijo-Lopes, Raphael; Markham, Alex F.; Dǒng, Evyn; Kay, Jackie; Saraswat, Manu; Vytla, Nikhil; Stark, Luke

doi:10.1145/3593013.3594134

Cited by 18 publications

(1 citation statement)

References 23 publications

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“…This nascent field builds on a long history of participatory approaches to computing research and development and has emerged in response to examples of sub-par performance of ML systems for marginalised groups. Participatory approaches have been enacted across each stage of ML design and development-from problem formulation to model evaluation-and include collaborative approaches to construct datasets [62,73], design and validate ML algorithms [57,72], and guide advocacy for algorithmic accountability [47,63]. At the same time, several authors have raised concerns about "participation-washing" [70], cooptation of participatory work [7], and the limited evidence across Participatory ML projects of equitable partnerships with participants [24,26,33].…”

Section: Introductionmentioning

confidence: 99%

From Fitting Participation to Forging Relationships: The Art of Participatory ML

Cooper,

Zafiroglu

2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Participatory machine learning (ML) encourages the inclusion of end users and people affected by ML systems in design and development processes. We interviewed 18 participation brokers-individuals who facilitate such inclusion and transform the products of participants' labour into inputs for an ML artefact or system-across a range of organisational settings and project locations. Our findings demonstrate the inherent challenges of integrating messy contextual information generated through participation with the structured data formats required by ML workflows and the uneven power dynamics in project contexts. We advocate for evolution in the role of brokers to more equitably balance value generated in Participatory ML projects for design and development teams with value created for participants. To move beyond 'fitting' participation to existing processes and empower participants to envision alternative futures through ML, brokers must become educators and advocates for end users, while attending to frustration and dissent from indirect stakeholders.

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Section: Introductionmentioning

confidence: 99%

From Fitting Participation to Forging Relationships: The Art of Participatory ML

Cooper,

Zafiroglu

2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Policy advice and best practices on bias and fairness in AI

Alvarez,

Colmenarejo,

Elobaid

et al. 2024

Ethics Inf Technol

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The literature addressing bias and fairness in AI models (fair-AI) is growing at a fast pace, making it difficult for novel researchers and practitioners to have a bird’s-eye view picture of the field. In particular, many policy initiatives, standards, and best practices in fair-AI have been proposed for setting principles, procedures, and knowledge bases to guide and operationalize the management of bias and fairness. The first objective of this paper is to concisely survey the state-of-the-art of fair-AI methods and resources, and the main policies on bias in AI, with the aim of providing such a bird’s-eye guidance for both researchers and practitioners. The second objective of the paper is to contribute to the policy advice and best practices state-of-the-art by leveraging from the results of the NoBIAS research project. We present and discuss a few relevant topics organized around the NoBIAS architecture, which is made up of a Legal Layer, focusing on the European Union context, and a Bias Management Layer, focusing on understanding, mitigating, and accounting for bias.

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Auditing and instructing text-to-image generation models on fairness

Friedrich,

Brack,

Struppek

et al. 2024

AI Ethics

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Generative AI models have recently achieved astonishing results in quality and are consequently employed in a fast-growing number of applications. However, since they are highly data-driven, relying on billion-sized datasets randomly scraped from the internet, they also suffer from degenerated and biased human behavior, as we demonstrate. In fact, they may even reinforce such biases. To not only uncover but also combat these undesired effects, we present a novel strategy, called Fair Diffusion, to attenuate biases during the deployment of generative text-to-image models. Specifically, we demonstrate shifting a bias in any direction based on human instructions yielding arbitrary proportions for, e.g., identity groups. As our empirical evaluation demonstrates, this introduced control enables instructing generative image models on fairness, requiring no data filtering nor additional training.

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Queer In AI: A Case Study in Community-Led Participatory AI

Cited by 18 publications

References 23 publications

From Fitting Participation to Forging Relationships: The Art of Participatory ML

From Fitting Participation to Forging Relationships: The Art of Participatory ML

Policy advice and best practices on bias and fairness in AI

Auditing and instructing text-to-image generation models on fairness

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