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为什么那么多公司都会犯招聘错误? – 译学馆
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为什么那么多公司都会犯招聘错误?

Inside bias: Why so many companies make big hiring mistakes

在现实中 我们太关注正在建造的事物
In this world where we focus so much on what we ’ re building,
以及如何建造
how we ’ re building it,
我认为我们应该退一步重新思考建造的目的所在
I think we need to take a step back and reconsider why we’re building
并且如何将科技变得人性化
and really humanize our technology,
将我们多元化的团队
really bring together diverse teams
与方法论以及人的思维方式结合起来
of methodologies and people and mindsets
那样我们就可以让我们的科技
so that we can take our technology and
更加适用于最基本的人类问题
actually apply it to the most fundamental human problems.
众所周知 当今人工智能受到广泛关注
Today the conversation is largely about artificial intelligence,
而我想讨论Fuzzie and Techie这本书中所提到的
and one of the concepts that I like to discuss in the book The Fuzzie and the Techie
人类智能的概念
is this concept of intelligence augmentations
即如何利用人工智能来扩大人类的能力
so thinking about using AI but using it in a way that’s augmenting the ability of humans
Kayak.com的创始人保罗·英格利什
so Paul English who was the creator of kayak.com
他是一个彻头彻尾的电子迷 同时他也称自己是一名AI现实主义者
he’s a techie through and through but he also calls himself an AI realist
他相信人工智能的潜力 但他也意识到
he’s somebody who believes in the promise of artificial intelligence but also realizes
并不是明天或者明年甚至下一个十年
that this is not something that tomorrow or next year or maybe perhaps in the next decade
人工智能就能完全拥有一个人
is going to completely take away from sort of from the
所能提供的特征与品质
the characteristics and the qualities of what a human can provide
因此他在波士顿建立了Lola公司
and so he’s now creating a company called Lola
而Lola公司在某种程度上
that’s based in Boston and Lola is
可以称为Kayak的2.0版本
is sort of Kayak 2.0,
Lola公司并不是发展线上旅游产业
where rather than trying to take the travel industry and put it online
实际上 保罗将事务交回给旅游机构来管理
he ’ s actually taking travel and putting it back into the hands of travel agents,
真人会通过电话
real people that are working on the phones dealing with
来处理打电话预定旅游的人
people that are calling in to book travel.
而他所做的就是将这些旅游机构与技术
and what he’s doing is he’s supplementing those travel agents with technology
和人工智能结合起来 真正实现“字母的翻转” 并且
with artificial intelligence, really “flippingthe letters” and trying to
作为一名AI现实主义者来利用人类智能
use intelligence augmentation as an AI realist
去提升旅游机构提供的服务质量
to sort of better the service that a travel agent can provide.
埃里克·科尔森 Stitch Fix 的首席算法官
Eric Colson, who is the Chief Algorithms Officer at Stitch Fix,
他使用机器学习 也用人工智能
he uses machine learning—he uses artificial intelligence,
却是用于增加人类设计师的数量
but to augment the human stylist.
因此 他们有六七十个数据科学家正在致力于制造学习算法的机器
So they have 60 or 70 data scientists working on creating machine learning algorithms,
但这些都用于补充这3400或3500个辅助设计的机器
but those are used to supplement the 34 3,500 stylist who have
这些机器有着各自的倾向来顺应潮流
their own propensity for delivering fashion they have their own biases as to the
这些倾向来自当地环境 年龄
the geography or the age or the
和对象人群的风格喜好
style preferences of somebody they might be servingclothing to.
因此 机器学习随着时间的推移的确能学习到人们的偏好
And so the machine learning actually learns the bias of the human over time
并且尝试通过增加给特定设计师
and tries to mitigate that bias by offsetting
的衣物选项来减缓偏向程度
the selection of clothing that they provide to that particular stylist.
我认为这的确是对于AI应用的一个有趣的例子
I think that’s a really interesting example of artificial intelligence not necessarily
AI不必回避设计师 反而能够帮助他们做到更好
taking away from that stylist but actually augmenting improving helping them perform better
而且我认为将字母A与I的翻转
and I think that flipping the letters from AI to IA
即从人工智能到人类智能的转变确实是当今人工智能的争论中我们该多加考虑的问题
is really something that we should be thinking more about today in the context of the AI debate.
我认为应该从就业需求和写出我们想雇用的工作描述开始
I think it starts with job requisition and writing sort of the job descriptions that we want to hire for.
并且我认为 当我们被申请人
And I think we are bombarded by applicants,
新简历以及数据的处理不断轰炸时
we ’ re bombarded by new resumes and “ data driven processes ”,
快速的解决方法当然是利用自然语言处理并找出关键词
and so the quick answer is to use natural language processing and screen for keywords,
将信息通过一个过滤器
to run things through a filter and
来筛选出包含与你们团队有关的五个关键词的简历
draw out the resumes that really hit the five key words that relate to your team.
而我认为这种做法造成了一种内部偏见
And I think what this does is it creates sort of an “inside bias” , where you’re creating
即你将所有相同观点 相同背景的人聚集在一起
and you’re bringing together people that all have sort of the same perspective the same backgrounds
这样就完全符合由丹尼尔·卡内曼
and it can really sort of create in the the sense of what Daniel Kahneman
2006诺贝尔奖的获得者在行为经济学中所提到的内部偏见的意义
the 2006 Nobel Prize winner in behavioral economics talks about is inside bias
我认为在某种程度上 可以同时考虑内部以及外部偏见
and I think to the extent that we can think about inside outside bias
尝试使小组中20%的成员拥有不同的观点
and trying to bring say 20% of the team from a different perspective
方向 方法或背景
from a different vector from a different methodology or background
而这些确实可以使这个小组具有多样性
that can really bring diversity to a team where,
假设你有一个数据科学小组
if you have a data science team,
而在这个小组中 大约80%的成员拥有完整的数据科学学习背景
80% of the people may make perfect sense to have them have complete backgrounds in
但对于剩下的20%的成员而言
in data science—but what ’ s to say that 20 percent of the team
他们不应该是哲学家 心理学家或人类学家吗?
shouldn’t be philosophers or psychologists or anthropologists?
并且我认为与其将它认为是谷歌的20%自由时间
And I think that sort of mentality of almost Google 20% time but thinking about it for
不如将它应用于产生20%的不同方法或观点
20% people time 20% difference of methodology or difference of perspective.
【音乐】
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视频概述

为什么那么多公司都会犯招聘错误?这个视频将会告诉你人类智能在行业中的应用。

听录译者

收集自网络

翻译译者

Licia

审核员

审核员 GK

视频来源

https://www.youtube.com/watch?v=t6sjyVDNa90

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