Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamics

By Donald Martin, Vinodkumar Prabhakaran, Jill A. Kuhlberg, Andrew Smart, William S. Isaac. 2020

75 citations recorded by the source corpus.

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.

Subfields: Computer Science Applications, Management of Technology and Innovation, Safety Research, Safety, Robustness & Interpretability, AI Ethics, Fairness & Society

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Citations in the corpus

Re-imagining Algorithmic Fairness in India and Beyond (2021, background): “Since outcomes of ML deployments can be difficult to predict, proposals for accountability include participatory design [109] and participatory problem formulation [128], sharing the responsibility for design-”
Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design (2022, background): “…throughout this framework – from envisioning potential harms, to feedback for the purpose of model improvement – but particularly so in release deliberation to better understand the risk landscape and mitigation strategies (Martin Jr et al., 2020; Ovadya and Whittlestone, 2019; Bruckman, 2020).”
Taxonomy of Risks posed by Language Models (2022, background): “One mitigation approach is to expand metrics and benchmarks to account for social context [125].”
Power to the People? Opportunities and Challenges for Participatory AI (2022, background): “The recent upswing in participatory tools have varied in approach and point in the machine learning product lifecycle, including: auditing methods for machine learning based interventions [48, 75], public consultation methods such as citizen juries [6, 83] or joint problem formulation [61], informat”
What We Can't Measure, We Can't Understand: Challenges to Demographic Data Procurement in the Pursuit of Fairness (2020, background): “the thinking about how the data should be used, it is both more likely that data will be employed in the interest of those it is about and that the fairness achieved will have material benefits for their lives [22, 29, 47, 54, 57, 77].”
Participation Is not a Design Fix for Machine Learning (2020, background): “[44], designers and technologists engage in episodic, short-term projects in which diverse stakeholders might be consulted at various stages of the process.”
Epistemic values in feature importance methods: Lessons from feminist epistemology (2021, background): “Participatory machine learning practices [54, 73] could be extended from participating in algorithm design to participating in the design of explanations, albeit with an awareness that participatory design is not a cure-all [72].”
The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice (2023, background): “…that participation can enable AI systems to better reflect the values, preferences, and needs of users and other impacted stakeholders, or more broadly, that participation will empower stakeholders in shaping the design of AI systems [3, 7, 19, 26, 33, 51, 61, 118, 137, 143, 172, 189, 198–200].”