Algorithmic Inheritance: Surname Bias in AI Decisions Reinforces Intergenerational Inequality

TL;DR

This study investigates how surnames influence AI decision-making across various high-stakes contexts, revealing biases that reinforce intergenerational inequality and highlighting the need for fairness-aware algorithms.

Contribution

It is the first to empirically analyze surname bias in AI decisions across different countries and contexts, demonstrating its impact and potential mitigation strategies.

Findings

Elite surnames increase perceptions of power, intelligence, and wealth in AI evaluations.

Perceived intelligence mediates the effect of surnames on AI decisions.

Providing candidate credentials reduces but does not eliminate surname bias.

Abstract

Surnames often convey implicit markers of social status, wealth, and lineage, shaping perceptions in ways that can perpetuate systemic biases and intergenerational inequality. This study is the first of its kind to investigate whether and how surnames influence AI-driven decision-making, focusing on their effects across key areas such as hiring recommendations, leadership appointments, and loan approvals. Using 72,000 evaluations of 600 surnames from the United States and Thailand, two countries with distinct sociohistorical contexts and surname conventions, we classify names into four categories: Rich, Legacy, Normal, and phonetically similar Variant groups. Our findings show that elite surnames consistently increase AI-generated perceptions of power, intelligence, and wealth, which in turn influence AI-driven decisions in high-stakes contexts. Mediation analysis reveals perceived intelligence as a key mechanism through which surname biases influence AI decision-making process. While providing objective qualifications alongside surnames mitigates most of these biases, it does not eliminate them entirely, especially in contexts where candidate credentials are low. These findings highlight the need for fairness-aware algorithms and robust policy measures to prevent AI systems from reinforcing systemic inequalities tied to surnames, an often-overlooked bias compared to more salient characteristics such as race and gender. Our work calls for a critical reassessment of algorithmic accountability and its broader societal impact, particularly in systems designed to uphold meritocratic principles while counteracting the perpetuation of intergenerational privilege.