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

arXiv: https://arxiv.org/abs/2005.07572 · HTML: https://mlsanity.com/paper/2005.07572/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2020.

## Built on by (most cited first, 30 of 31)

- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [Re-imagining Algorithmic Fairness in India and Beyond](https://mlsanity.com/paper/2101.09995/) (2021)
- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Values](https://mlsanity.com/paper/2310.07629/) (2023)
- [Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities](https://mlsanity.com/paper/2102.04257/) (2021)
- [Envisioning Communities: A Participatory Approach Towards AI for Social Good](https://mlsanity.com/paper/2105.01774/) (2021)
- [A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms](https://mlsanity.com/paper/2206.14983/) (2022)
- [From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML](https://mlsanity.com/paper/2210.03535/) (2022)
- [Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir"](https://mlsanity.com/paper/2111.01122/) (2021)
- [Healthsheet: Development of a Transparency Artifact for Health Datasets](https://mlsanity.com/paper/2202.13028/) (2022)
- [A Human Rights-Based Approach to Responsible AI](https://mlsanity.com/paper/2210.02667/) (2022)
- [Towards Responsible Natural Language Annotation for the Varieties of Arabic](https://mlsanity.com/paper/2203.09597/) (2022)
- [Going public: the role of public participation approaches in commercial AI labs](https://mlsanity.com/paper/2306.09871/) (2023)
- [Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints](https://mlsanity.com/paper/2306.13738/) (2023)
- [Designing Fiduciary Artificial Intelligence](https://mlsanity.com/paper/2308.02435/) (2023)
- [Beyond Model Interpretability: Socio-Structural Explanations in Machine Learning](https://mlsanity.com/paper/2409.03632/) (2024)
- [Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities](https://mlsanity.com/paper/2301.05753/) (2023)
- [Manifestations of Xenophobia in AI Systems](https://mlsanity.com/paper/2212.07877/) (2022)
- [What Makes An Expert? Reviewing How ML Researchers Define "Expert"](https://mlsanity.com/paper/2411.00179/) (2024)
- [System Safety Engineering for Social and Ethical ML Risks: A Case Study](https://mlsanity.com/paper/2211.04602/) (2022)
- [Concrete Safety for ML Problems: System Safety for ML Development and Assessment](https://mlsanity.com/paper/2302.02972/) (2023)
- [The Equitable AI Research Roundtable (EARR): Towards Community-Based Decision Making in Responsible AI Development](https://mlsanity.com/paper/2303.08177/) (2023)
- [Advancing Community Engaged Approaches to Identifying Structural Drivers of Racial Bias in Health Diagnostic Algorithms](https://mlsanity.com/paper/2305.13485/) (2023)
- [Cultural Perspectives and Expectations for Generative AI: A Global Survey Approach](https://mlsanity.com/paper/2603.05723/) (2026)
- [Beyond Abstract Compliance: Operationalising trust in AI as a moral relationship](https://mlsanity.com/paper/2601.22769/) (2026)
- [Expansive Participatory AI: Supporting Dreaming within Inequitable Institutions](https://mlsanity.com/paper/2211.12434/) (2022)
- [Fairness: from the ethical principle to the practice of Machine Learning development as an ongoing agreement with stakeholders](https://mlsanity.com/paper/2304.06031/) (2023)
- [Examining the Values Reflected by Children during AI Problem Formulation](https://mlsanity.com/paper/2309.15839/) (2023)
- [FairTargetSim: An Interactive Simulator for Understanding and Explaining the Fairness Effects of Target Variable Definition](https://mlsanity.com/paper/2403.06031/) (2024)
- [Who Leads? Comparing Human-Centric and Model-Centric Strategies for Defining ML Target Variables](https://mlsanity.com/paper/2510.25974/) (2025)

## Abstract (arXiv)

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.