Importance of Spatial Fairness in Decision-Making Systems

Conceitos essenciais

Urgent need to address spatial fairness in decision-making systems due to location biases.

Resumo

Abstract

Urges consideration of spatial fairness due to correlation with protected characteristics.

Introduction

Data-driven decision-making systems prevalent but can replicate historical biases.

Problems unique to spatial data

Dimensionality, computing spatial network distance, continuity of space, MAUP, spatial autocorrelation.

Limitations of current spatial fairness work

Legal soundness, limitations in techniques like closing the loop and taking agency away from people.

Guidelines and Future Directions

Close the loop, avoid disparate impact, consider location as a protected attribute, be resistant to MAUP.

Conclusion

Argues for importance of addressing spatial fairness and outlines guidelines for future research.

Estatísticas

"Despite location being increasingly used in decision-making systems employed in many sensitive domains such as mortgages and insurance."
"The adoption of data-driven decision-making systems has skyrocketed across the board in the last two decades."
"For example, neighborhoods in the United States have been historically correlated with race."

Citações

"Everything is related to everything else, but near things are more related than distant things." - Waldo Tobler

Principais Insights Extraídos De

Spatial Fairness

by Nripsuta Ani... às arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14040.pdf

Perguntas Mais Profundas

How can fair-AI researchers ensure that their methodologies address unfairness present due to location's correlation with protected characteristics?

Fair-AI researchers can ensure that their methodologies address unfairness related to location by closing the loop in their research. This involves conducting empirical experiments to demonstrate how their proposed methods reduce bias associated with protected attributes like race or national origin. Researchers should compare the extent of bias before and after implementing their methodology, considering trade-offs with accuracy on outcomes. Additionally, they should assess disparate impact, ensuring that unintentional harm is not caused to individuals belonging to protected classes.

Should location be considered a protected or immutable characteristic in decision-making scenarios?

In decision-making scenarios where location plays a significant role in perpetuating biases related to legally protected characteristics, such as race or national origin, it may be beneficial to consider location as either a protected attribute or an immutable characteristic. By treating location as immutable for certain groups—such as low-income families who lack agency in choosing their residence—fairness can be better achieved. This approach aligns with anti-discrimination laws that protect against discrimination based on immutable characteristics and ensures equitable treatment for all individuals regardless of where they live.

How can fair-AI work integrate public policy recommendations to reduce inequities effectively?

To integrate public policy recommendations effectively into fair-AI work and reduce inequities, researchers should familiarize themselves with relevant policies and guidelines from fields like public policy and economic development. By incorporating practices recommended for reducing disparities into AI algorithms and decision-making systems, researchers can help prevent unintended perpetuation of inequalities. Furthermore, auditing tools can be developed within AI systems to detect spatial inequities based on legal standards and societal norms outlined in public policies. This integration ensures that fair-AI techniques align with legal requirements while promoting fairness and equity in decision-making processes.