Algorithmic Bias and Structural Inequality
Othelia E. Lee · 2026
This chapter explores algorithmic bias and its deep ties to structural inequality, offering a critical social work perspective on one of artificial intelligence’s (AI’s) most urgent challenges. It defines algorithmic bias and examines its sources—biased data, flawed design, and systemic inequities. The chapter illustrates how AI functions like prediction and classification can reinforce discrimination in human services. It emphasizes how structural inequality shapes algorithmic outcomes and calls for equity-centered design. Strategies for detecting and mitigating bias are presented, including audits and inclusive data practices. Social workers are positioned as ethical advocates who can challenge bias and promote fairness in AI systems that impact vulnerable populations.