Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context
By Donald Martin, Vinodkumar Prabhakaran, Jill A. Kuhlberg, Andrew Smart, William S. Isaac. 2020
42 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Management Science and Operations Research, Safety, Robustness & Interpretability
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Citations in the corpus
- Measurement and Fairness (2019, neutral): “These operationalizations are appealing to computer scientists because of their quantitative nature and because they operate within the boundaries of a single computational system, without reference to the broader societal context in which the system is situated [38].”
- Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets (2021, background): “[20] stresses the importance of, and develops approaches to modeling societal context to evaluate and mitigate unfairness of a system.”