Big data is not always trustworthy from "summary" of Weapons of Math Destruction by Cathy O'Neil
In the age of big data, we are often led to believe that more data means better outcomes. We trust that the algorithms and models built on massive amounts of information will lead us to objective and accurate results. However, this blind faith in big data can be misleading. Just because something is based on a large amount of data does not automatically make it reliable or trustworthy. One of the main reasons why big data is not always trustworthy is the potential for bias. The data collected and used in algorithms are often influenced by human decisions and prejudices. If the data itself is biased, then the outcomes produced by the algorithms will also be biased. This perpetuates and even exacerbates existing inequalities and injustices in society. Another issue with big data is the lack of transparency in how the algorithms work. Many times, these algorithms are treated as black boxes that produce results without any explanation of how they arrived at those conclusions. This lack of transparency makes it difficult to question or challenge the results, leading to blind acceptance of potentially flawed information. Furthermore, the constant need for more data can lead to a phenomenon known as "data dredging." This is where researchers or companies search through massive amounts of data until they find a pattern or correlation that fits their preconceived notions. By cherry-picking data in this way, the results become unreliable and misleading.- While big data has the potential to revolutionize industries and improve decision-making, it is essential to approach it with caution and skepticism. We must acknowledge the limitations and biases inherent in big data and work towards creating more transparent and accountable algorithms. Only then can we ensure that the outcomes produced by big data are truly trustworthy and beneficial to society.
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