数据科学 (Data Science)

Nina was close to tears when she accused Nick Gibb of ruining her life. Nina is an 18 year old about to leave school and go on to higher education; Gibb is the UK government’s schools minister.

妮娜(Nina)指责尼克·吉布(Nick Gibb)毁了自己的生活时差点流泪。 妮娜(Nina)今年18岁，即将离开学校继续接受高等教育； 吉布(Gibb)是英国政府的学校部长。

The occasion was a live BBC radio programme called “Any Questions”, a popular show where politicians and others discuss questions posed by the public.

场合是英国广播公司(BBC)的直播节目“任何问题”，这是一个受欢迎的节目，政界人士和其他人讨论公众提出的问题。

Nina was on track to be accepted by the most prestigious veterinary college in the country; she just needed to get the right grades in her A-level exams and her teachers predicted that she would indeed get those grades.

妮娜(Nina)有望被该国最负盛名的兽医学院录取； 她只需要在A级考试中取得正确的成绩即可，她的老师预测她的确会取得这些成绩。

But, because of the pandemic, the exams never took place and instead the government sanctioned the use of an algorithm to assign grades to students, instead.

但是，由于大流行，考试从未进行过，相反，政府批准使用一种算法为学生分配成绩。

This algorithm reduced the grades for nearly 40% of students. Nina’s were so much lower than the ones predicted for her that, not only would she not be able to get into her preferred college, but no other college would accept her, either.

该算法降低了将近40％的学生的成绩。 妮娜(Nina)的学历比她预期的要低得多，不仅她不能进入自己喜欢的大学，而且其他任何大学也不会接受她。

Mr Gibb was reassuring and told the audibly upset Nina that this was a mistake that would be put right.

Gibb先生放心，并向Nina倾诉不安，这是可以纠正的错误。

And it was. A few days later, after much argument, protest and controversy, and to the great relief of many students who were in a similar situation to Nina, the algorithm was dumped in favour of student grades predicted by schools.

是的。 几天后，经过多次争论，抗议和争议，并使处于与Nina相似境地的许多学生大为欣慰的是，该算法被弃用，以支持学校预测的学生成绩。

So how did the government get itself into this mess?

那么，政府如何陷入困境呢？

The concern was that just using predicted grades would result in grade inflation because teachers tend to be more optimistic about their students’ abilities than is borne out in actual exam results.

令人担心的是，仅使用预测的成绩会导致成绩膨胀，因为与实际考试结果相比，教师更倾向于对学生的能力持乐观态度。

So an algorithm was devised that was based, not so much on the academic record of the individual student, but on the record of the school. A results profile was constructed for the school — how many As did previous cohorts achieve, how many Bs, C’s and so on — and the individual students were positioned on that profile. The ‘exam results’ were then calculated for each student depending on their position in the profile.

因此，设计了一种算法，该算法不仅基于单个学生的学习成绩，而且还基于学校的成绩。 为学校构建了一个结果概要文件-以前的队列达到了多少，B，C的数目等等，并且每个学生都位于该概要文件上。 然后根据每个学生在个人资料中的位置为他们计算“考试结果”。

Using statistics from previous years to predict future results is not an unusual thing to do. Indeed, statistical models like these are very useful for government and corporate planning.

使用前几年的统计数据来预测未来的结果并不是一件寻常的事情。 确实，像这样的统计模型对于政府和公司计划非常有用。

However, while the predicted results may have been in line with previous years and might well reflect what the outcome of this year’s exam results might have been, generally, there are at least two glaring problems.

但是，尽管预测结果可能与前几年保持一致，并且可能很好地反映了今年考试结果的结果，但通常至少存在两个明显的问题。

The first is that some schools are getting better, so judging their performance on the previous year would produce a worse result than is fair. This would affect all of the students at that school.

首先是一些学校的状况正在好转，因此，从上一年的表现来看，其结果会不公平。 这将影响该学校的所有学生。

Secondly, not all cohorts are the same. There will always be a number of high flyers in a school but this number will vary. If there are fewer in a particular school this year, then the statistical approach taken would artificially promote some more mediocre students into that category. They probably won’t complain.

其次，并不是所有的队列都是一样的。 在学校中，总是会有很多高级传单，但是这个数字会有所不同。 如果今年某所学校的学生人数减少，那么采用的统计方法将人为地将一些中等水平的学生晋升为该类别。 他们可能不会抱怨。

But if there are a larger number of gifted students in this year’s cohort then they will be unfairly downgraded and, like Nina, be awarded grades below those that they could have achieved in a real exam.

但是，如果今年的队列中有更多的天才学生，那么他们将被不公平地降级，并且像Nina一样，被授予的分数低于他们在真实考试中可以达到的成绩。

These additional gifted students may well be cheated out of their place at a good university or college.

这些额外的有天赋的学生很可能会被一所好的大学或学院骗走。

In a further twist, the algorithm was not applied to small cohorts because the statistics for these smaller groups are not reliable. In the small classes, which are often private schools, the teacher predicted grades were used. Thus smaller private schools got the benefit of generous teacher assessment while the larger state schools did not. Not an equitable situation.

更进一步，该算法不适用于较小的同类群，因为这些较小的群体的统计数据不可靠。 在通常是私立学校的小班教学中，使用了老师预测的成绩。 因此，较小的私立学校获得了慷慨的教师评估的好处，而较大的公立学校则没有。 并非公平的情况。

So should we chuck out algorithms altogether? No. There are many situations where the use of algorithms and statistics are entirely suitable for prediction purposes. How many beds should a hospital leave free during the flu season? Previous numbers of flu cases can inform the calculation. When Netflix or Amazon recommend a movie to watch or a product to buy, they are using statistics from people who they think are like you. But, if they get it wrong you end up watching a movie that you don’t enjoy or rejecting a product that you don’t want to buy. This is not life changing.

那么我们应该完全放弃算法吗？ 不可以。在许多情况下，算法和统计信息的使用完全适合预测目的。 流感季节医院应该腾出几张床？ 以前的流感病例数可以为计算提供依据。 当Netflix或Amazon建议观看电影或购买产品时，他们会使用他们认为与您相似的人的统计数据。 但是，如果他们弄错了，那么您最终会看不喜欢的电影，或者拒绝您不想购买的产品。 这不会改变生活。

When statistics are used to make decisions that are profound, using someone else’s data just won’t do. It might produce an overall pattern that is satisfying to the designers, or policy makers, but it can unfairly disadvantage individuals.

当使用统计数据做出有意义的决策时，仅使用其他人的数据是行不通的。 它可能会产生令设计者或决策者满意的整体模式，但可能不公平地使个人处于不利地位。

Making decisions about someone’s personal life by using data from people from a similar background is wrong and unethical. Nina’s grades were calculated by looking at students who were similar to her. But they were not her. If such important decisions are to be made about someone’s life then it is only that person’s data that should be taken into account.

通过使用来自相似背景的人的数据来决定某人的个人生活是错误和不道德的。 妮娜的成绩是通过查看与她相似的学生得出的。 但是他们不是她。 如果要对某人的生活做出如此重要的决定，那么仅应考虑该人的数据。

And maybe it should not be an algorithm that makes that decision.

也许它不应该是做出决定的算法。