A Way to Detect Bias
Paul Graham describes a method for detecting bias in selection processes without knowing the applicant pool: if a selection process is biased against a group, those selected from that group must be better and thus will outperform other successful applicants. Using the example of venture capital bias against female founders, he notes that First Round Capital's own data showed startups with female founders outperformed by 63%, inadvertently revealing bias. The method works when a random sample of selected applicants exists, their subsequent performance is measured, and the groups have roughly equal ability distributions.
October 2015
This will come as a surprise to a lot of people, but in some cases it's possible to detect bias in a selection process without knowing anything about the applicant pool. Which is exciting because among other things it means third parties can use this technique to detect bias whether those doing the selecting want them to or not.
You can use this technique whenever (a) you have at least a random sample of the applicants that were selected, (b) their subsequent performance is measured, and (c) the groups of applicants you're comparing have roughly equal distribution of ability.
How does it work? Think about what it means to be biased. What it means for a selection process to be biased against applicants of type x is that it's harder for them to make it through. Which means applicants of type x have to be better to get selected than applicants not of type x. [1] Which means applicants of type x who do make it through the selection process will outperform other successful applicants. And if the performance of all the successful applicants is measured, you'll know if they do.
Of course, the test you use to measure performance must be a valid one. And in particular it must not be invalidated by the bias you're trying to measure. But there are some domains where performance can be measured, and in those detecting bias is straightforward. Want to know if the selection process was biased against some type of applicant? Check whether they outperform the others. This is not just a heuristic for detecting bias. It's what bias means.
2015年10月
这会让很多人感到意外，但在某些情况下，即使对申请人背景一无所知，也能检测出选拔过程中的偏见。这令人兴奋，因为这意味着第三方可以使用这种方法来检测偏见，无论选拔方是否愿意。
只要满足以下条件，你就可以使用这项技术：(a) 你至少拥有被选中者的随机样本，(b) 他们的后续表现可衡量，并且 (c) 你比较的申请人群体能力分布大致相等。
它是如何运作的？思考一下偏见的含义。选拔过程对x类申请人存在偏见，意味着他们更难通过选拔。这意味着x类申请人必须比非x类申请人更优秀才能被选中。[1] 因此，成功通过选拔的x类申请人将比其他成功的申请人表现更好。如果所有成功申请人的表现都被衡量，你就能知道他们是否表现更优。
当然，你用来衡量表现的测试必须是有效的。特别地，它不能因为你想衡量的偏见而失效。但在某些领域，表现是可以衡量的，检测偏见因此变得简单。想知道选拔过程是否对某类申请人存在偏见？检查他们是否比其他申请人表现更好。这不仅仅是检测偏见的一种启发式方法，它本身就是偏见的含义。
For example, many suspect that venture capital firms are biased against female founders. This would be easy to detect: among their portfolio companies, do startups with female founders outperform those without? A couple months ago, one VC firm (almost certainly unintentionally) published a study showing bias of this type. First Round Capital found that among its portfolio companies, startups with female founders outperformed those without by 63%. [2]
例如，许多人怀疑风险投资公司对女性创始人存在偏见。这很容易检测：在其投资组合公司中，有女性创始人的初创公司是否比没有的表现更好？几个月前，一家风投公司（几乎肯定是无意中）发表了一项研究，展示了这类偏见。First Round Capital 发现，在其投资组合中，有女性创始人的初创公司比没有的表现高出63%。[2]
The reason I began by saying that this technique would come as a surprise to many people is that we so rarely see analyses of this type. I'm sure it will come as a surprise to First Round that they performed one. I doubt anyone there realized that by limiting their sample to their own portfolio, they were producing a study not of startup trends but of their own biases when selecting companies.
I predict we'll see this technique used more in the future. The information needed to conduct such studies is increasingly available. Data about who applies for things is usually closely guarded by the organizations selecting them, but nowadays data about who gets selected is often publicly available to anyone who takes the trouble to aggregate it.
我一开始说这项技术会让很多人感到意外，是因为我们很少看到此类分析。我相信First Round会对自己进行了这样一项分析感到惊讶。我怀疑那里没有人意识到，通过将样本限制在自己的投资组合中，他们得出的其实不是创业趋势研究，而是自身在选择公司时的偏见研究。
我预测未来我们会看到这项技术被更频繁地使用。进行此类研究所需的信息越来越容易获取。关于谁申请了某件事的数据通常被选拔组织严格保密，但如今关于谁被选中的数据往往对任何愿意花精力去汇总的人来说都是公开可得的。
[1] This technique wouldn't work if the selection process looked for different things from different types of applicants—for example, if an employer hired men based on their ability but women based on their appearance.
[2] As Paul Buchheit points out, First Round excluded their most successful investment, Uber, from the study. And while it makes sense to exclude outliers from some types of studies, studies of returns from startup investing, which is all about hitting outliers, are not one of them.
[1] 如果选拔过程对不同类型申请人要求不同——例如，雇主根据能力雇佣男性，但根据外貌雇佣女性——那么这项技术将失效。
[2] 正如Paul Buchheit指出的，First Round将最成功的投资Uber排除在研究之外。虽然排除异常值在某些类型的研究中是合理的，但创业投资回报研究（其核心就是捕捉异常值）不属于此类。
Thanks to Sam Altman, Jessica Livingston, and Geoff Ralston for reading drafts of this.
感谢Sam Altman、Jessica Livingston和Geoff Ralston阅读本文的草稿。