SocioEval: A Template-Based Framework for Evaluating Socioeconomic Status Bias in Foundation Models

Socioeconomic bias in AI systems remains largely unmeasured: While researchers have developed frameworks to evaluate racial and gender bias in large language models, socioeconomic status bias has been significantly overlooked despite having "widespread implications in the real world"—SocioEval addresses this critical gap.

AI bias varies dramatically by decision context: The study found that lifestyle-related judgments showed 10 times higher bias rates than education-related decisions, suggesting that some domains are far more vulnerable to class-based discrimination than others when using AI systems for decision-making.

Current AI safeguards have significant blind spots: While deployment protections effectively prevent explicit discrimination, the research reveals these safeguards are "brittle to domain-specific stereotypes," meaning AI systems can still discriminate against people based on socioeconomic status in subtle, context-specific ways that current safety measures don't catch.

Bias rates vary substantially across AI models: Testing 13 frontier language models revealed bias rates ranging from 0.42% to 33.75%—a 80-fold difference—indicating that model choice significantly impacts whether an AI system will discriminate based on socioeconomic status.

Systematic evaluation frameworks are now available for accountability: SocioEval provides organizations with a practical, scalable tool to audit their AI systems for class-based bias across 8 themes and 18 topics, enabling more responsible deployment of AI in high-stakes decision-making domains.