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HumanCentered Machine Learning: Measuring Fairness Dong Nguyen2021recap! Last time: Intro to fairnessDual use What do we mean with fairness? Harms: Allocative harms, representational harms Feedback
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Towardsdatasciencecomanalysing-fairness-inanalysing fairness in machine refers to the analysis of fairness in machine learning algorithms and models.
Researchers, data scientists, and organizations working with machine learning are required to analyze and report fairness in machine learning models.
One can fill out towardsdatasciencecomanalysing-fairness-inanalysing fairness in machine by conducting fairness audits, evaluating different fairness metrics, and addressing potential biases in the machine learning model.
The purpose of towardsdatasciencecomanalysing-fairness-inanalysing fairness in machine is to ensure that machine learning models are fair, unbiased, and do not discriminate against specific groups or individuals.
The report on towardsdatasciencecomanalysing-fairness-inanalysing fairness in machine must include details on the dataset used, the machine learning algorithm employed, the fairness metrics measured, and any actions taken to mitigate bias.
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