# A Systems Thinking Approach to Algorithmic Fairness

arXiv: https://arxiv.org/abs/2412.16641 · HTML: https://mlsanity.com/paper/2412.16641/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 62 references that are in the map.
- First posted on arXiv 21 Dec 2024.

## Builds on (most cited first, 8 of 8)

- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Direct and Indirect Effects](https://mlsanity.com/paper/1301.2300/) (2013)
- [Avoiding Discrimination through Causal Reasoning](https://mlsanity.com/paper/1706.02744/) (2017)
- [On the Apparent Conflict Between Individual and Group Fairness](https://mlsanity.com/paper/1912.06883/) (2019)
- [A Clarification of the Nuances in the Fairness Metrics Landscape](https://mlsanity.com/paper/2106.00467/) (2021)
- [Causal Conceptions of Fairness and their Consequences](https://mlsanity.com/paper/2207.05302/) (2022)
- [The Disparate Impacts of College Admissions Policies on Asian American Applicants](https://mlsanity.com/paper/2308.04449/) (2023)
- [Debiasing Alternative Data for Credit Underwriting Using Causal Inference](https://mlsanity.com/paper/2410.22382/) (2024)

## Built on by (most cited first, 1 of 1)

- [An Experimental Study on Fairness-aware Machine Learning for Credit Scoring Problems](https://mlsanity.com/paper/2412.20298/) (2024)

## Abstract (arXiv)

Systems thinking provides us with a way to model the algorithmic fairness problem by allowing us to encode prior knowledge and assumptions about where we believe bias might exist in the data generating process. We can then encode these beliefs as a series of causal graphs, enabling us to link AI/ML systems to politics and the law. This allows us to combine techniques from machine learning, causal inference, and system dynamics in order to capture different emergent aspects of the fairness problem. We can use systems thinking to help policymakers on both sides of the political aisle to understand the complex trade-offs that exist from different types of fairness policies, providing a sociotechnical foundation for designing AI policy that is aligned to their political agendas and with society's shared democratic values.