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Mitigation of Data Bias Through Fair Feature Selection Methods Ginel DorleonTo cite this version: Ginel Dorleon. Mitigation of Data Bias Through Fair Feature Selection Methods. Artificial Intelligence

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What is mitigation of data bias?

Mitigation of data bias refers to the process of identifying, addressing, and reducing biases that can affect the quality and accuracy of data analysis and outcomes.

Who is required to file mitigation of data bias?

Entities involved in data collection, analysis, or reporting, including businesses, organizations, and researchers, are typically required to file mitigation of data bias if their data practices are subject to regulatory compliance or standards.

How to fill out mitigation of data bias?

To fill out mitigation of data bias, organizations should provide a detailed description of the identified biases, the methods used to address them, and the outcomes of their mitigation efforts in a standardized format as required by relevant authorities.

What is the purpose of mitigation of data bias?

The purpose of mitigation of data bias is to enhance the fairness, accuracy, and reliability of data-driven decisions and to ensure that data products do not perpetuate inequality or misinformation.

What information must be reported on mitigation of data bias?

Organizations must report details on identifying biases, methods of mitigation used, the impact of those methods, assessments of data integrity, and ongoing plans for monitoring and improvement.

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