Algorithmic bias: Difference between revisions

Revision as of 10:38, 15 February 2025

Revision as of 03:04, 10 March 2025

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===== Racial bias =====

Racial bias refers to the tendency of machine learning models to produce outcomes that unfairly discriminate against or stereotype individuals based on race or ethnicity. This bias often stems from training data that reflects historical and systemic inequalities. For example, AI systems used in hiring, law enforcement, or healthcare may disproportionately disadvantage certain racial groups by reinforcing existing stereotypes or underrepresenting them in key areas. Such biases can manifest in ways like facial recognition systems misidentifying individuals of certain racial backgrounds or healthcare algorithms underestimating the medical needs of minority patients. Addressing racial bias requires careful examination of data, improved transparency in algorithmic processes, and efforts to ensure fairness throughout the AI development lifecycle.<ref>{{Cite web |last=Lazaro |first=Gina |date=May 17, 2024 |title=Understanding Gender and Racial Bias in AI |url=https://www.sir.advancedleadership.harvard.edu/articles/understanding-gender-and-racial-bias-in-ai |access-date=December 11, 2024 |website=Harvard Advanced Leadership Initiative Social Impact Review}}</ref><ref>{{Cite journal |last=Jindal |first=Atin |date=September 5, 2022 |title=Misguided Artificial Intelligence: How Racial Bias is Built Into Clinical Models |url=https://bhm.scholasticahq.com/article/38021-misguided-artificial-intelligence-how-racial-bias-is-built-into-clinical-models |journal=Journal of Brown Hospital Medicine |volume=2 |issue=1 |doi=10.56305/001c.38021 |access-date=December 11, 2024|doi-access=free |pmc=11878858 }}</ref>

=== Technical ===