Bias and Fairness in Artificial Intelligence: Methods and Mitigation Strategies
DOI:
https://doi.org/10.36676/jrps.v15.i3.1425Keywords:
Bias, Fairness, Artificial Intelligence, Mitigation StrategiesAbstract
Artificial intelligence (AI) has quickly evolved from a sci-fi idea to a crucial part of modern technology, impacting a number of industries like healthcare, banking, education, and law enforcement. Fairness and bias issues with AI systems have drawn a lot of attention as they grow increasingly prevalent in everyday life. In artificial intelligence, "bias" refers to the systematic and unjust discrimination against particular groups of individuals. Prejudices in training data or those unintentionally introduced during algorithm development are common examples of bias. Contrarily, fairness is the idea that every person should have equal access to opportunities and treatment regardless of society or personal traits.
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