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Marrying Fairness and Explain ability in Supervised Learning
Premyslid GrabowiczNicholas PerelloAarshee MishraUniversity of Massachusetts Amherst, Massachusetts, USA
grabowicz@cs.umass.eduUniversity
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Define the objective of the machine
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Select the appropriate dataset for training
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Use transparent algorithms that can be easily understood
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Provide explanations for the decisions made by the machine
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Companies that want to ensure fairness and transparency in their decision-making processes
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What is fair and explainable machine?
Fair and explainable machine refers to a technology or system that operates in a transparent and accountable manner, providing clear explanations for its decisions.
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To fill out fair and explainable machine, organizations must ensure transparency in their algorithms, provide explanations for decision-making processes, and address bias and fairness issues.
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Information such as data sources, algorithm explanations, model outputs, and potential biases must be reported on fair and explainable machine.
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