- The paper introduces a participatory method integrating Community-Based System Dynamics to mitigate bias during ML problem formulation.
- It employs visual tools like causal loop diagrams to transparently model dynamic societal complexities and stakeholder perspectives.
- The study demonstrates that engaging marginalized voices early enhances fairness and leads to more robust and equitable ML systems.
Participatory Problem Formulation for Fairer Machine Learning Through Community-Based System Dynamics
The paper presents an innovative approach to improving fairness in ML by addressing a critical yet often overlooked stage in ML system development: problem formulation. Traditionally, this phase involves crafting a strategic goal and translating it into ML problems. However, the authors highlight that biases introduced here can lead to significant unfairness in system outcomes, particularly impacting marginalized communities. As an alternative, they propose the integration of Community-Based System Dynamics (CBSD) to engage stakeholders, especially those typically excluded from these discussions, in the problem formulation stage.
Central to the argument is that current methods fail by not adequately capturing the dynamic complexities of society and by excluding essential stakeholder perspectives. For example, high-stakes domains such as healthcare and criminal justice have often been influenced by implicit biases built into ML algorithms, resulting in unfair outcomes, such as racial bias in risk assessments. A cited paper (Obermeyer et al.) illustrates this point by analyzing how healthcare costs were used as an inadequate proxy for health needs, leading to systematic bias against African-Americans—a consequence of neglecting socio-economic disparities in healthcare access.
The authors explore two major pitfalls in current problem formulation practices: a lack of recognition for the dynamic complexity of real-world issues and the exclusion of diverse stakeholder experiences. These gaps result in ML systems that inadequately address the systemic biases prevalent within problem domains.
CBSD offers a participatory framework that involves stakeholders in developing causal models of complex systems. Unlike other modeling techniques such as Structural Causal Models or Causal Bayesian Networks, CBSD is rooted in system dynamics. This approach emphasizes feedback and the dynamic interplay of variables over time, allowing for a comprehensive understanding of societal and technological complexities. The unique feature of CBSD is its participatory nature, which facilitates the inclusion of marginalized voices, thus offering richer, more accurate problem formulations.
The methodology employs visual tools such as causal loop diagrams and stock and flow diagrams, making the modeling process more transparent and accessible. These diagrams help illustrate and quantify variable interactions and feedback loops, providing stakeholders with a platform to develop deeper insights into causal structures and potential impacts of proposed interventions. CBSD encourages a culture of collaboration, where diverse stakeholders can iteratively refine causal models, critically reflect on assumptions, and propose equitable solutions.
Furthermore, CBSD has successfully been applied in various domains such as public health and social work, proving its potential for impact. The paper argues for extending these practices to the field of ML, particularly for fairness interventions. By centering discussions on causal inferences and their potential biases, CBSD aims to foster a formalized approach to problem formulation that is both inclusive and cognizant of long-term impacts.
In conclusion, this work underscores the necessity of revisiting how problems are formulated in ML contexts. By incorporating CBSD, stakeholders can proactively address biases at the earliest stages, thus paving the way for more equitable and robust ML systems. The authors advocate for a shift towards participatory model development that acknowledges and integrates diverse experiences, reflecting a commitment to fairness and ethical ML practices. This approach not only highlights the technical intricacies of causal modeling but also stresses the importance of community engagement and representation in the creation of just ML interventions. Future developments in AI should increasingly focus on embedding such frameworks to ensure equitable technology deployment.