【導語】本文是一篇說明文，介紹了大數據雖然廣泛應用於人們生活的方方麵麵，但它也存在一定的弊端，我們在使用大數據時一定要確保數據的可靠性。
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We live in the age of the algorithm (算法). Increasingly, the decisions that affect our lives— where we go to school, whether we get a car loan, how much we pay for health insurance— are being made not by humans, but by mathematical models.
One application that has become particularly common is the use of algorithms to evaluate job performance. Sarah, a teacher who, despite being widely respected by her students, their parents and her colleagues, was fired because she performed poorly according to an algorithm. When an algorithm rates you poorly, you are immediately branded as an underperformer and there is rarely an opportunity to appeal against those judgments. In many cases, methods are considered secrets and no details are shared. And data often seems convincing.
As a matter of fact, the belief that school performance in America is declining is based on a data mistake. A Nation at Risk is the report that rang the initial alarm bells about declining SAT (Scholastic Assessment Test) scores. Yet if they had taken a closer look, they would have noticed that the scores in each smaller group were increasing. The reason for the decline in the average score was that more disadvantaged kids were taking the test. However, due to the data mistake, teachers as a whole were judged to be failing.
Wall Street is famous for its mathematicians who build complex models to predict market movements and develop business plans. These are really smart people. Even so, it is not at all uncommon for their models to fail. The key difference between those models and many of the ones being used these days is that Wall Street traders lose money when their data models go wrong. However, as CV Neil points out in her book, the effects of widely —used machine — driven judgments are often not borne by those who design the algorithms, but by everyone else.
As we increasingly rely on machines to make decisions, we need to ask these questions: What assumptions are there in your model? What hasn’t been taken into account? How are we going to test the effectiveness of the conclusions? Clearly, something has gone terribly wrong. When machines replace humans to make a judgment, we should hold them to a high standard. We should know how the data was collected. And when numbers lie, we should stop listening to them.
本文到此結束，希望對大家有所幫助呢。
