The Hidden Biases in Big Data
批判了“数据原教旨主义”，即认为大数据总能反映客观真理的观点，指出相关性不等于因果，数据本身并不客观。
This looks to be the year that we reach peak big data hype. From wildly popular big data conferences to columns in major newspapers, the business and science worlds are focused on how large datasets can give insight on previously intractable challenges. The hype becomes problematic when it leads to what I call “data fundamentalism,” the notion that correlation always indicates causation, and that massive data sets and predictive analytics always reflect objective truth. Former Wired editor-in-chief Chris Anderson embraced this idea in his comment, “with enough data, the numbers speak for themselves.” But can big data really deliver on that promise? Can numbers actually speak for themselves?