LLamaRecipes/recipes/experimental/long-context/H2O/data/PaulGrahamEssays/bias.txt

October 2015This 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.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]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.
Notes[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.
Thanks to Sam Altman, Jessica Livingston, and Geoff Ralston for reading
drafts of this.