Recent research on algorithmic fairness has highlighted that the problem formulation phase of ML system development can be a key source of bias that has significant downstream impacts on ML system fairness outcomes. However, very little attention has been paid to methods for improving the fairness efficacy of this critical phase of ML system development. Current practice neither accounts for the dynamic complexity of high-stakes domains nor incorporates the perspectives of vulnerable stakeholders. In this paper we introduce community based system dynamics (CBSD) as an approach to enable the participation of typically excluded stakeholders in the problem formulation phase of the ML system development process and facilitate the deep problem understanding required to mitigate bias during this crucial stage.
This paper addresses the question of how to align AI systems with human values and situates it within a wider body of thought regarding technology and value. Far from existing in a vacuum, there has long been an interest in the ability of technology to 'lock-in' different value systems. There has also been considerable thought about how to align technologies with specific social values, including through participatory design-processes. In this paper we look more closely at the question of AI value alignment and suggest that the power and autonomy of AI systems gives rise to opportunities and c…
Collaborative Participatory Research with LLM Agents in South Asia: An Empirically-Grounded Methodological Initiative and Agenda from Field Evidence in Sri Lanka
Community-Led AI Integration for Wildfire Risk Assessment: A Participatory AI Literacy and Explainability Integration (PALEI) Framework in Los Angeles, CA
The University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；California State University, Los Angeles（加州州立大学洛杉矶分校）
"Taking Stock at FAccT": Using Participatory Design to Co-Create a Vision for the Fairness, Accountability and Transparency Community
Northeastern University Boston United States；Munich Center for Machine Learning (MCML) Munich Germany；University of Mannheim Mannheim Germany；StickFlux Labs \& University of Chicago United States；Northeastern University；Munich Center for Machine Learning (MCML)；University of Mannheim；StickFlux Labs \& University of Chicago
Premised on the logic that more, high-quality information on majority world women's lives will improve the effectiveness of interventions addressing gender inequality, mainstream development institutions have invested heavily in gender data initiatives of late. However, critical empirical and theoretical investigations into gender data for development policy and practice are lacking. Postcolonial feminist theory has long provided a critical lens through which to analyse international development projects that target women in the majority world. However, postcolonial feminism remains underutili…
In participatory budgeting, communities collectively decide on the allocation of public tax dollars for local public projects. In this work, we consider the question of fairly aggregating the preferences of community members to determine an allocation of funds to projects. This problem is different from standard fair resource allocation because of public goods: The allocated goods benefit all users simultaneously. Fairness is crucial in participatory decision making, since generating equitable outcomes is an important goal of these processes. We argue that the classic game theoretic notion of …
Going PLACES: Participatory Localized Red Teaming for Text-to-Image Safety in the Global South
Google DeepMind（谷歌DeepMind）；Yonsei University（延世大学）；Imperial College（帝国理工学院）；University of Wisconsin–Stevens Point（威斯康星州立大学斯普林特分校）；Google Research（谷歌研究）；Harvard University（哈佛大学）
Warwick University（华威大学）；Alan Turing Institute for Data Science and AI（艾伦·图灵数据科学与人工智能研究所）；Oxford University（牛津大学）
In recent years participatory budgeting (PB) in Scotland has grown from a handful of community-led processes to a movement supported by local and national government. This is epitomized by an agreement between the Scottish Government and the Convention of Scottish Local Authorities (COSLA) that at least 1% of local authority budgets will be subject to PB. This ongoing research paper explores the challenges that emerge from this 'scaling up' or 'mainstreaming' across the 32 local authorities that make up Scotland. The main objective is to evaluate local authority use of the digital platform Con…
As Machine Learning (ML) systems becomes more ubiquitous, ensuring the fair and equitable application of their underlying algorithms is of paramount importance. We argue that one way to achieve this is to proactively cultivate public pressure for ML developers to design and develop fairer algorithms -- and that one way to cultivate public pressure while simultaneously serving the interests and objectives of algorithm developers is through gameplay. We propose a new class of games -- ``games for fairness and interpretability'' -- as one example of an incentive-aligned approach for producing fai…
Australian National University（澳大利亚国立大学）；Google LLC（谷歌有限责任公司）；University of California, Irvine（加州大学欧文分校）；Google（谷歌）；Google Research（谷歌研究院）
HCI researchers have been gradually shifting attention from individual users to communities when engaging in research, design, and system development. However, our field has yet to establish a cohesive, systematic understanding of the challenges, benefits, and commitments of community-collaborative approaches to research. We conducted a systematic review and thematic analysis of 47 computing research papers discussing participatory research with communities for the development of technological artifacts and systems, published over the last two decades. From this review, we identified seven the…
The World Wide recipe: A community-centred framework for fine-grained data collection and regional bias operationalisation
University of Oxford（牛津大学）；Microsoft（微软公司）；Independent researcher（独立研究者）；University of Colorado Boulder（科罗拉多大学博尔德分校）；Imperial College London（伦敦帝国理工学院）；Faculty of Law, University of Oxford（牛津大学法学院）
