  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. Such oversimplified mathematical models
abstract away the underlying societal context where ML models are conceived,
developed, and ultimately deployed. As fairness itself is a socially
constructed concept that originates from that societal context along with the
model inputs and the models themselves, a lack of an in-depth understanding of
societal context can easily undermine the pursuit of ML fairness. 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 will help
researchers and product developers to 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
sociotechnical frame that incorporates diverse mental models and associated
causal theories in modeling the problem and solution space for ML-based
products. Finally, we identify community based system dynamics (CBSD) as a
powerful, transparent and rigorous approach for practicing CCTF during all
phases of the ML product development process. We conclude with a discussion of
how these systems theoretic approaches to understand the societal context
within which sociotechnical systems are embedded can improve the development of
fair and inclusive ML-based products.
