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Machine Learning Bias in Computer Vision: Why do I have to care? LaranjeiraVirgnia Fernandes MotaJefersson Alex dos SantosComputer Science Department Universidade Federal de Minas Gerais Belo Horizonte, Brazil camilalaranjeira@dcc.ufmg.brColegio Tecnico Universidade Federal de Minas Gerais Belo Horizonte, Brazil virginia@teiacoltec.orgComputer Science Department Universidade Federal de Minas Gerais Belo Horizonte, Brazil jefersson@dcc.ufmg.brAbstractMachine Learning bias is an issue with two...
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What is machine learning bias in?
Machine learning bias refers to systematic errors that occur in the predictions or results of machine learning algorithms, typically arising from the data used to train these models. Bias can be due to various factors including skewed training data, incorrect assumptions in the model, or improper handling of features.
Who is required to file machine learning bias in?
Organizations and practitioners involved in developing or deploying machine learning models that affect decision-making processes, particularly those in sensitive areas like finance, healthcare, and employment, may be required to assess and report on machine learning bias.
How to fill out machine learning bias in?
To address machine learning bias, organizations should conduct a careful analysis of their data sets for imbalances, use bias detection tools, document their findings, and implement corrective measures such as data augmentation or model fine-tuning. Proper documentation of the methodology and results should also be included in the filing.
What information must be reported on machine learning bias in?
Reported information must typically include a description of the data sources used, the methodologies employed for bias detection, the identified biases, mitigation strategies implemented, and an assessment of the model's performance across different demographic groups.
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