In recent years, the integration of artificial intelligence (AI) and machine learning into critical sectors like healthcare has raised significant concerns about algorithmic bias. A notable example is a 2019 study that found a widely used healthcare risk-prediction algorithm exhibited racial bias. The algorithm relied on healthcare spending as a proxy for medical need, inadvertently disadvantaging Black patients who, due to systemic inequalities, often have lower healthcare expenditures despite higher health risks. This led to Black patients being less likely to receive necessary care management services, highlighting how biased algorithms can perpetuate existing health disparities. scientificamerican.com
The implications of such biases extend beyond healthcare. In the criminal justice system, algorithms used to assess recidivism risk have been found to exhibit racial biases, potentially leading to unfair sentencing and parole decisions. Similarly, in education, algorithms employed to evaluate teacher performance based on student test scores have been criticized for reinforcing existing inequalities, as they may not account for the diverse challenges faced by students in different socioeconomic backgrounds. These instances underscore the critical need for transparency, accountability, and continuous evaluation in the development and deployment of algorithmic systems to prevent the exacerbation of societal biases. time.com