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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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Fair and explainable machine refers to a technology or system that operates in a transparent and accountable manner, providing clear explanations for its decisions.
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.
Information such as data sources, algorithm explanations, model outputs, and potential biases must be reported on fair and explainable machine.
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