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

arXiv: https://arxiv.org/abs/2006.09663 · HTML: https://mlsanity.com/paper/2006.09663/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2020.

## Built on by (most cited first, 18 of 18)

- [Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets](https://mlsanity.com/paper/2106.10328/) (2021)
- [Safeguarding Large Language Models: A Survey](https://mlsanity.com/paper/2406.02622/) (2024)
- [Building Guardrails for Large Language Models](https://mlsanity.com/paper/2402.01822/) (2024)
- [Evaluation Gaps in Machine Learning Practice](https://mlsanity.com/paper/2205.05256/) (2022)
- [Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models](https://mlsanity.com/paper/2206.08325/) (2022)
- [Ecosystem Graphs: The Social Footprint of Foundation Models](https://mlsanity.com/paper/2303.15772/) (2023)
- [Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems](https://mlsanity.com/paper/2101.09385/) (2021)
- [The four-fifths rule is not disparate impact: a woeful tale of epistemic trespassing in algorithmic fairness](https://mlsanity.com/paper/2202.09519/) (2022)
- [Towards a multi-stakeholder value-based assessment framework for algorithmic systems](https://mlsanity.com/paper/2205.04525/) (2022)
- [Decolonial AI Alignment: Openness, Vi'sedsa-Dharma, and Including Excluded Knowledges](https://mlsanity.com/paper/2309.05030/) (2023)
- [Thinking Beyond Distributions in Testing Machine Learned Models](https://mlsanity.com/paper/2112.03057/) (2021)
- [FairSense: Long-Term Fairness Analysis of ML-Enabled Systems](https://mlsanity.com/paper/2501.01665/) (2025)
- [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)
- [Deploying clinical machine learning? Consider the following...](https://mlsanity.com/paper/2109.06919/) (2021)
- [(Unfair) Norms in Fairness Research: A Meta-Analysis](https://mlsanity.com/paper/2407.16895/) (2024)
- [SYMBIOSIS: Systems Thinking and Machine Intelligence for Better Outcomes in Society](https://mlsanity.com/paper/2503.05857/) (2025)
- [The Initial Screening Order Problem](https://mlsanity.com/paper/2307.15398/) (2023)

## Abstract (arXiv)

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.