
A venture fund's experiment in human-free investing - rafaelc
https://www.bloomberg.com/news/features/2018-05-01/white-male-vcs-tend-to-fund-white-male-entrepreneurs-could-robots-do-better
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xevb3k
“if the algorithms liked what they saw, the venture fund would back
them...Similar tactics have brought promising results in other competitive
fields. The most famous example comes from the 1970s, when five major
orchestras began requiring musicians to stand behind a screen while
auditioning.”

These things are in no way similar.

The article seems to pre-suppose that an algorithm can not be biased. The
truth is, if the algorithm is trained on past deals then it could easily
encode bias. More than this, it can give plausible deniability to
biases/prejudiced behavior because “the algorithm did it”.

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nl
You have missed most of the context of the article there: "Carroll laughs as
she recalls the thread, without seeming particularly amused. “I was just like,
‘Says no one about a male entrepreneur, ever.’ ”

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bsder
And this actually almost makes me dismiss her outright. _Everybody_ raising
money has heard those.

 _I_ have personally heard: "Not enthusiastic enough/Too enthusiastic" (Irony:
two different people sitting at the _same_ presentation) "Too little/too much
experience" "Not enough/too many generalists." It goes on and on ...

You learn to ignore the excuse and move on--the excuse is irrelevant.

"No" is "no". Move on.

"Maybe" is "no". Move on.

"Yes" is no until you cash the check and it clears.

~~~
nl
That’s not the point. Or maybe it’s exactly the point.

The point is that doing the equivalent of blind auditions might solve it for
everyone. It is similar enough to the blind auditions example anyway to make
the “it is no way similar” argument clearly wrong.

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xevb3k
If you want to make it like a blind audition, then do that. Either just review
anonymized decks, or do the pitches over anonymized email.

This is not a blind audition... it’s something quite different.

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lordnacho
It's interesting how they've already spotted a number of possible problems
with using an algo to do VC, but there isn't really a compelling solution yet.

There are a number of parallels with the time when I was trading fixed income
at a hedge fund. We had a senior guy looking at the output of various
opportunity scanners, and deciding what to do.

There's several problems with this approach.

\- The human is always out to prove himself. If you don't override the system
now and again, what's the point of you? This means the humans are always on
the looking for some special one-off condition they can claim.

\- The algo dev stops short of where he could go with it. You ought to be
fully automating it, but you don't because you need to leave something on the
table. There's a number of data problems that you just don't get around to
solving because it's tedious and you aren't going to use it.

\- The VC guys have a much worse data problem, by the looks of it. Not every
startup will fill out the form. If they don't need your money, no form. If
they crash early, no form. After they fill out the form, how do you track what
happened to them? Seems like a big problem. Also if you're going to use ML you
need a fairly large number of rows. Not just filled out forms, but also labels
for how things turned out. And the more features you collect, the more
labelled rows you'll want.

So there's a real risk of falling into the pseudo-systematic hole here. You
take the data that you have and make conclusions that are very close to your
initial priors. Basically you end up with stylized "facts" that aren't
necessarily true, just believed.

Seems like a they've thought about these things though, will be interesting to
see what happens.

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y_molodtsov
In the end this is still an investment, and the stock purchase agreement will
probably include terms that would require companies to upload their P/L each
quarter. They could easily track progress based on that.

~~~
lordnacho
That doesn't tell you how firms you didn't invest in are doing. Presumably you
want to know how well your selection algo is doing, and you can't without
having an idea of how your non investments are going.

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drewvolpe
What I find most interesting here is that for a relatively small amount of
money (compared with their fund size), Social Capital is creating an amazing
dataset about founders and startups. It will be really useful for them a few
years from now to be able to go back and see what was most predictive of
successful startups.

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siliconc0w
It's really easy to overfit and create biased algorithms, especially with how
small their 'startup' training set is likely to be. That is, of course, if
they're doing ML and not just encoding their own investor 'intuition' into
software.

Still, I agree the weight of personal relationships and human-powered-
decision-making guiding the 'tech' industry is a bit ironic.

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nukeop
Why would it be a problem that white males get funded? Besides, I'm willing to
bet that they'll find out that the algorithm also picks white males, for
objective reasons.

