Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context

Machine learning (ML) fairness research tends to focus primarily on mathematically-based interventions on often opaque algorithms or models and/or their immediate inputs and outputs. Recent re-search has pointed out the limitations of fairness approaches that rely on oversimplified mathematical models that abstract away the underlying societal context where models are ultimately deployed and from which model inputs and complex socially constructed concepts such as fairness originate. In this paper, we outline three new tools to improve the comprehension, identification and representation of societal context. First, we propose a complex adaptive systems(CAS) based model and definition of societal context that may help researchers and product developers expand the abstraction boundary of ML fairness work to include societal context. Second, we introduce collaborative causal theory formation (CCTF)as a key capability for establishing a socio-technical frame that incorporates diverse mental models and associated causal theories in modeling the problem and solution space for ML-based products. Finally, we identify system dynamics (SD) as an established, transparent and rigorous framework for practicing CCTF during all phases of the ML product development process. We conclude with a discussion of how these systems-based approaches to understanding the societal context within which socio-technical systems are embedded can improve the development of fair and inclusive ML-based products.

• Data Mining and Modeling

• Machine Intelligence