Working Paper: NBER ID: w26739
Authors: Adair Morse; Karen Pence
Abstract: Technology has changed how discrimination manifests itself in financial services. Replacing human discretion with algorithms in decision-making roles reduces taste-based discrimination, and new modeling techniques have expanded access to financial services to households who were previously excluded from these markets. However, algorithms can exhibit bias from human involvement in the development process, and their opacity and complexity can facilitate statistical discrimination inconsistent with antidiscrimination laws in several aspects of financial services provision, including advertising, pricing, and credit-risk assessment. In this chapter, we provide a new amalgamation and analysis of these developments, identifying five gateways whereby technology induces discrimination to creep into financial services. We also consider how these technological changes in finance intersect with existing discrimination and data privacy laws, leading to our contribution of four frontlines of regulation. Our analysis concludes that the net effect of innovation in technological finance on discrimination is ambiguous and depends on the future choices made by policymakers, the courts, and firms.
Keywords: Discrimination; Technology; Household Finance; Algorithms
JEL Codes: G2; G21; G28; G5; K2; K38; O33
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Technology (L63) | Discrimination Reduction (J79) |
| Human Involvement in Algorithm Design (C90) | Algorithmic Bias (D91) |
| Embedded Biases in Training Datasets (J70) | Discriminatory Outcomes (J79) |
| Statistical Discrimination Practices (J71) | Disparities in Credit Risk Assessment (G21) |
| Advertising Technology (M38) | Discriminatory Outcomes (J79) |
| Regulatory Uncertainties (G18) | Discrimination Outcomes (J71) |