Striving for fairness in data analysis from "summary" of Weapons of Math Destruction by Cathy O'Neil
In the quest for fairness in data analysis, one must first acknowledge the biases that are inherent in the data itself. Data is not neutral; it is the result of human decisions and actions, which are often influenced by societal biases and prejudices. These biases can manifest in various forms, such as sampling bias, measurement error, or selection bias. To strive for fairness in data analysis, one must actively seek out and address these biases. This requires a critical examination of the data collection process, as well as an understanding of the context in which the data was collected. By identifying and correcting for biases in the data, one can ensure that the results of the analysis are more accurate and representative of ...
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