Algorithmic Bias and Place of Residence: Feedback Loops in Financial and Risk Assessments Tools

This article explores how criminal risk-need assessment algorithms (e.g., COMPAS) and financial scoring systems (e.g., FICO) create feedback loops that perpetuate systemic biases, disproportionately affecting already financially marginalized groups. It examines the intersection of these tools, particularly how factors like place of residence, financial instability, and access to resources influence both systems. Using a theoretical critique, this study indirectly analyzes (1) criminological theories, (2) algorithmic design principles, and (3) evidentiary standards. The criminological theories considered—including Social Class and Crime, Strain Theory, Subcultural Perspectives, Labeling and Marxist/Conflict Theories, Control Theories, and Differential Association Theory—share a consensus that environmental factors contribute to crime. While this research does not aim to verify their conclusions, it investigates how algorithmic models incorporate personal financial data and place of residence. It also examines the relevance of these to observing non-virtuous behaviors, as supported by the previously mentioned criminological theories, although the findings of these theories may differ regarding the levels of relevance of the environment to criminal occurrences. Additionally, evidentiary standards and numerical reasoning help assess how these inputs shape potentially biased and unfair scores. Findings suggest that low scores in one system exacerbate low scores in the other, creating a cyclical disadvantage. This reinforces economic and social inequities, calling for greater scrutiny, transparency, and fairness in algorithmic design and application. Ignoring these issues risks deepening poverty, restricting credit access, and increasing incarceration rates among financially marginalized communities. By highlighting these feedback loops, this study aims to inform academic research and policy reforms to mitigate algorithmic bias and its far-reaching consequences.