Question

In Weapons of Math Destruction, data scientist Cathy O’Neil explains how big data exists everywhere in our lives, and that we hardly even notice it until it affects us directly. One application that has become particularly common is the use of algorithms (算法) to evaluate job performance.

She tells the story of Sarah Wysocki, 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 O’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.

1.What does the example of Sarah Wysocki mainly show?

A. The drawback of big data.    B. The popularity of big data.

C. The new challenge teachers face.    D. The misunderstanding about algorithms.

2.Widely-used machine-driven judgments ________.

A. never make any economic loss

B. can lead to many innocent victims

C. are more complicated than Wall Street’s data models

D. can go wrong more easily than Wall Street’s data models

3.What does the author suggest in the last paragraph?

A. Making decisions without machines.    B. Making sure that the data are reliable.

C. Making the algorithms more effective.    D. Making the data and algorithms public.

Answer

1. A

2. B

3. B

【解析】

本文是一篇说明文，介绍了数据学家Cathy O’Neil在新书《数字杀伤性武器》中指出大数据虽然广泛应用于人们生活的方方面面，但它也存在一定的弊端，我们在使用大数据时一定要确保数据的可靠性。

1.推理判断题。根据第一段中的“She tells the story of Sarah Wysocki, 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.”可知，尽管Sarah Wysocki深受学生、家长和同事的尊敬，但还是因为评定工作表现的算法显示她表现不佳而被解雇了，举这个例子就是为了证明大数据评定工作表现的算法存在弊端，不能真正反映一个人的工作表现，故A项正确。

2.推理判断题。根据第三段中的“the effects of widely-used machine-driven judgments are often not borne by those who design the algorithms, but by everyone else.”可知，广泛使用的机器驱动判断的影响往往不是由设计算法的人承担而是由其他人承担的，由此推知，广泛使用的机器驱动判断会导致很多无辜的受害者，故B项正确。

3.推理判断题。根据最后一段中的“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.”可知，用机器代替人类做判断时，我们需要对机器定一个高标准，我们应该数据是如何收集的，当数字撒谎时，我们应该停止倾听，也就是说我们要确保数据的可靠性，故B项正确。