Secondary Bounded Rationality: A Theory of How Algorithms Reproduce Structural Inequality in AI Hiring
For researchers and practitioners in AI ethics and fairness, this work provides a theoretical framework linking algorithmic bias to sociological theories of capital and bounded rationality, though it is primarily conceptual without empirical validation.
The paper introduces 'secondary bounded rationality' to explain how AI hiring systems encode and amplify structural inequalities by optimizing for biased proxies of competence, such as elite credentials and network homophily, transforming historical biases into seemingly meritocratic outcomes.
AI-driven recruitment systems, while promising efficiency and objectivity, often perpetuate systemic inequalities by encoding cultural and social capital disparities into algorithmic decision making. This article develops and defends a novel theory of secondary bounded rationality, arguing that AI systems, despite their computational power, inherit and amplify human cognitive and structural biases through technical and sociopolitical constraints. Analyzing multimodal recruitment frameworks, we demonstrate how algorithmic processes transform historical inequalities, such as elite credential privileging and network homophily, into ostensibly meritocratic outcomes. Using Bourdieusian capital theory and Simon's bounded rationality, we reveal a recursive cycle where AI entrenches exclusion by optimizing for legible yet biased proxies of competence. We propose mitigation strategies, including counterfactual fairness testing, capital-aware auditing, and regulatory interventions, to disrupt this self-reinforcing inequality.