A survey on bias and fairness in machine learning.

Analyzing bias and fairness in machine learning involves investigating how the algorithms used in these systems might unintentionally favor certain groups or outcomes over others. This survey aims to understand and address these potential biases to ensure that machine learning technologies are fair and equitable for everyone. Researchers in this field investigate issues such as algorithmic discrimination, fairness, and transparency to make sure that machine learning systems consider diverse perspectives and do not perpetuate harmful biases. Conducting surveys and studies in this area is crucial for creating machine learning models that are unbiased and fair for all individuals.