Can AI remove implicit bias from the hiring process?

人工智能（AI）能否清除雇用过程中的内隐偏见？

“Remove”, entirely remove? No.

“清除” 完全清除？不能

But as I understand I've had multiple people tell me that it's already reducing the impact of implicit bias, so they're already happy with what they're seeing.

不过据我所知 有很多人告诉我AI已经在减少内隐偏见的影响了 他们对此感到很开心

So what is implicit bias, first of all?

首先来说一下 什么是内隐偏见？

It's important to understand that implicit bias and explicit bias are two different things.

理解内隐偏见和外在偏见是两码事儿 这很重要

Implicit bias is stuff that you're not conscious of; you're not aware of it;

内隐偏见是一种你无意识的东西；你意识不到它的存在；

it's hard for you to control; it's probably impossible for you to control.

你很难控制它；或许你根本不可能控制得了它

It's impossible for you to control, right now, on demand.

现在 你不可能控制它

You might be able to alter it by exposing yourself to different situations or whatever

或许你可以通过让自己暴露在不同的环境中等等

and changing what we in machine learning call priors—so changing your experiences.

并改变机器学习中所谓的先知先觉来改变它——从而改变你的经历

So maybe if you see more women in senior positions you'll become less implicitly sexist, or something like that.

所以如果你在高层职位中看到更多女性 你的性别歧视心里可能就会减弱之类的

But anyway, explicit bias is like “I’m going to choose to only work with women” or

但总之 外在偏见就是“我选择只跟女性一起共事”或者

“I’m going to choose only to work with men” and I know that and I'm conscious about it.

“我只跟男性一起共事” 而且我知道我是这么想的 我是有意识的

So HR departments are reasonably good at getting people who hopefully honestly are saying

所以人力资源部门相当擅长识别出真诚有希望地说

“yeah I'm not going to be racist or sexist or whatever-ist,

“嗯 我不是种族主义者或性别歧视者或任何主义者

I'm not going to worry about how long somebody's name is or what the country of origin of someone of their ancestors is.”

我不在乎别人的名字有多长 也不在乎别人的祖先来自哪个国家”

So hopefully HR people can spot the people who sincerely are neutral, at least at the explicit level.

所以但愿人力资源能够鉴别出真正中立的人 至少从外在上看是这样的

But at the implicit level, there's a lot of evidence that something else might be going on.

但在内隐偏见上 有很多证据表明其实另有隐情

Again, we don't know for sure if it's implicit or explicit, but what we do know is that in the paper we did in 2017

再说一次 我们并不确定它是内在还是外在的 但我们知道的是在2017年所做的一篇论文中

one of my co-authors Aylin Caliskan had this brilliant idea of looking at the resume data.

我的一位共同作者艾林·卡斯利坎想到了查看简历数据的好主意

So there's this famous study that showed that you have identical resumes

于是就出现了这项很出名的研究 里面用到的简历完全相同

and the only thing you do is have more African-American names versus European American names,

唯一的区别就是让更多非裔美国人的名字和欧裔美国人的名字进行对比

and the people with European American names get 50 percent more calls in to interview with nothing else changed.

在其他内容完全相同的情况下 有欧裔美国人名字的人得到的面试机会要多出50%

And so now people are talking about “whitening” their CVs just so they get that chance to interview.

所以现在人们在想要“白化”他们的简历 只为得到更多面试机会

So anyway, it looks by the measures that we used with the vector spaces

总之 根据我们对这个矢量空间所采用的方法

as if the data and the implicit bias that also explains implicit bias also explains those choices on the resume.

似乎数据和内隐偏见也解释了简历上的选择

So does that mean people are looking at it and explicitly saying, “Oh I think that's an African-American?”

这是否意味着人们看着简历直接说“哦 我觉得这是个非裔美国人”？

Or were they just going through huge stacks of CVs and some didn't jump out at them in the same ways that others did?

还是说他们只是在浏览成千上万的简历 而有一些简历就那样被埋没了？

Because we're pretty sure when it comes down to like they're all sitting in the room together that that point was okay.

因为我们相当确定他们都是一起坐在一个房间里 这没关系

And so what the AI is doing for them is it's helping them pick out the characteristics they're looking for and ignoring the other characteristics.

而AI所做的事就是帮助人类选出他们需要的特点 同时忽略他特点

So they're helping them detect the things that they wanted to be:

所以AI在帮助人们发现他们需要的东西：

when they were sitting in the room with multiple eyes looking at something,

它们坐在房间里 有很多眼睛同时盯着某些东西

that they were looking at the right starting place and then they're able to find -

它们在找一个合适的起点 然后才能找到——

they're finding people that were falling through the cracks.

它们在找那些被忽视的人们

A lot of people have trouble, that there's not enough good people applying or that they thought there weren't enough good people applying,

很多人会遇到困难 要么是优秀的申请人不够多 要么是申请的人不够优秀

but actually, they were missing people because they didn't see the qualifications buried in the other stuff when they're leafing through these stacks.

但其实 他们错过了很多人 因为他们在浏览简历时 没有看到埋没在其他东西里的一些资质

So a lot of people are reporting that they have great data or they're very pleased with the results,

所以很多人都说他们的数据很棒或者他们对结果很满意

but that's privately and it's off the record and I can't get anyone to go on the record.

但那只是私下的 并没有记录在案 而我没法让任何人去公开发言

I just recently at Princeton, the Center for Information Technology Policy ran a meeting about AI, and somebody, again in Chatham House,

最近我在普林斯顿信息技术政策中心开了一场关于AI的会 有个人 也是查塔姆研究所的

I can't say who it was, but an organization that's sort of between corporate and—

我不能说是谁 但是一个类似于公司之类的机构——

anyway it's a special kind of organization, they said that they're going to try and do this and so I begged them to document it.

总之 是一个很特别的机构 他们说要尝试这么做 我请求他们一定要把数据记录下来

I said look you're in a different situation you don't have ordinary customers,

我说 听着 你们现在情况不一样了 你们的客人不是普通人

please document the results fully and then publish papers about it so we can really see what the outcomes are.

请把所有结果记录下来 然后发布相关论文 让我们真正看看结果是什么样的

So I hope we'll have that data, but so far I could only tell you that people are saying it really is working.

我希望我们能有那样的数据 但目前我只能告诉你人们说它真的有效果

One of the possible shortfalls of that kind of situation, well first of all being sure that you can eliminate bias that way, no;

那种情况一种可能的缺点是 首先确定你可以以这种方式消除偏见 不可能的；

there's all kinds of ways you can accidentally pick up on things.

有很多种方式会让你无意间注意到某些事

So even if you don't have gender you might recognize gender from the name, for example.

比如 即便上面没有写性别 你也可以通过名字辨别出性别

So there's ways that machine learning picks up on regularities that are illegal and, again,

机器学习有一种注意到非法规律的方法 而且

you have to do your own auditing and make sure that that isn't happening.

你得自己做审计 确保不会发生这种事

And I guess that's the biggest concern.

我想这就是最大的隐患吧

Of course anytime you scan something and make it digital the net makes it amenable to hacking, so you have to be careful about that.

当然了 你可以随时扫描一些东西 把它变成数字形式 但网络会让它容易遭受攻击 所以你得注意点

And I guess the biggest thing is don't believe that just because you've automated part of a process you've made it fair.

我觉得最大的事就是不要以为你把某个过程部分自动化了 就相当于你把它变公平了

You have to keep checking—Just like anything else you keep going to improve.

你得时刻检查——就像任何其他你会持续改善的事一样

But yeah when you put these things in front of you and when you write them down, then yeah you have the potential to keep improving.

不过没错 当你把事情都拿出来 写下来 那你就有持续进步的潜力

I guess there's one other thing, which I haven't mentioned,

还有另一件我没有提到的事

which is that once you've automated the process you do open the door for somebody who is, say, an evil racist to go in

那就是一旦你把过程自动化了 就相当于给某些人 比如邪恶的种族主义者打开了大门

and actually tweak things and make it so that you get all one race.

让他们有机会扭曲事实 让你只能得到单一人种的人才

So you need to make sure that there's adequate oversight and regular auditing because people worry about accidentally introducing bias,

所以你要确保有足够的监督 并经常审计 因为人们会担心无意间引进了一些偏见

and that's good, we should worry about that, but we should be really worried about deliberately introducing bias.

这很好 我们确实应该担心 但我们真正该担心的是故意引进偏见

That's the thing that I think, again, because people think artificial intelligence is like space aliens that are kind of -

这才是我觉得 因为人们觉得人工智能就像外星人一样有点——

it's actually almost like sort of the Greco Roman or Nordic gods or something,

其实它有点像希腊罗马或者北欧神明之类的东西

like “Maybe we can pray to them correctly and they'll give us what we want, but they're capricious and we're not sure.”

就像“或许我们可以恰当地向他们祈祷 他们会达成我们的心愿 但它们有些变化无常 这我们可不敢保证”

No, it's not like that.

不 并不是那样的

It really is something that we have an opportunity to try to fix it, and it works in systematic ways,

它是让我们有可能解决问题的机会 它的运作方式很系统

but it's important to understand that people are writing it, and that means that some people will make mistakes,

但需要注意的是它是由人为写出来的 这就意味着它也可能会出错

some people will be sloppy, some people will do what they seriously think is the best thing, but it actually isn't legal

有些人比较草率 有些人会做他们自以为最好但却是违法的事

and some people will go out of their way to do bad things

还有些人会越线去做坏事

because they're just vandals or because that's how they got elected or whatever.

因为他们就是坏人 或者只有那样他们才能当选等等