We need to be aware of biases in big data from "summary" of The Internet of Us: Knowing More and Understanding Less in the Age of Big Data by Michael P. Lynch
When using big data to make decisions or draw conclusions, it is crucial to be aware of the biases that may be present in the data. Big data sets are often vast and complex, containing information from a wide range of sources and contexts. As a result, these data sets can easily reflect the biases and assumptions of those who collected, curated, or analyzed the data. One common source of bias in big data is selection bias, which occurs when certain types of data are systematically excluded from the dataset. For example, if a study only collects data from people who have access to the internet, the resulting dataset may not be representative of the population as a whole. Similarly, if a social media platform uses an algorithm that favors certain types of content over others, the resulting data may be skewed towards those preferences. Another source of bias in big data is confirmation bias, which occurs whe...
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