4 ms·

“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

by xevb3k 8y ago

“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”.

lev99 8y ago
> The CaaS form already asks applicants for some personal information, such as LinkedIn profiles and educational background. Carroll is resistant to the idea of using such data to glean insights about businesses. Because well-educated white men have the easiest time raising money today, any model using demographics to predict success would favor them—the opposite of her intention. Still, Social Capital is experimenting with building personalized models anyway, though it hasn’t implemented any yet They specifically talk about this risk, current data models only use business data and they discuss the dangers of using personal data. It seems to me that data like customer loyalty and cash on hand are perfectly fine to use and don’t come with any direct gender or ethnicity bias.
- xevb3k 8y ago
  My point is that a number of features can be well correlated with gender/other social factors and result in training bias into the algorithm. The following article discusses this in the context of policing for example: https://boingboing.net/2015/12/02/racist-algorithms-how-big-dat.html https://boingboing.net/2015/12/02/racist-algorithms-how-big-...
  - lev99 8y ago
    My point is the article doesn't ignore it, and are specifically targeting data points less likely to have gender/race bias. You presented your comment like it was a point of view not covered in the article.
nl 8y ago
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.’ ”
- bsder 8y ago
  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 8y ago
    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.
    - xevb3k 8y ago
      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.
  - fwdpropaganda 8y ago
    > You learn to ignore the excuse and move on (...) That's actually a great attitude in almost everything in life: recognize that some things are best modeled as a random variable, and if you fail there might not be a causal explanation and you just have to keep trying. The downside is that you might be given valuable information (feedback) and you might dismiss it as noise. So it's a real skill telling signal from noise.
vasilipupkin 8y ago
the point that the algorithm is or isn't biased. the point is, humans are also training their experience on past deals, so the issue is, is the algorithm more biased, on average, than humans, or less? I have absolutely no doubt that an average VC fund decision making process can be replaced by an algorithm. Best vc funds - that's a tougher call. But in general, algorithmic investing is superior to seat of pants investing.
- xevb3k 8y ago
  If a human is biased, there are individuals whose attitude can be corrected, and if necessary pressure can be put on individuals to correct the situation (politically or otherwise). With an algorithm, you show the inputs, show the outputs and say “no bias, it’s all automated”. Look, part of the problem might be that at seed stage the optimal short term strategy might be to be biased. If series A,B investors are sexist or racist, then that’s going to make things harder for those companies to progress. An algorithm might pick up on that, particularly if it can find features correlated with sex, race, or some other factor that investors may be biased against. As to your point regarding the average VC. It’s possible that selecting companies at random might do better than the average VC. I suspect if we did, the societal outcomes might be better.
candiodari 8y ago
You forget the last source of bias : the bias could be on a lower level. For instance, there could be some sort of bias, hidden to the algorithm, that makes certain decisions succeed and others fail in a biased way, but for instance if we're talking companies, state-backed companies succeed, foreign companies suddenly fail. There could be a real world bias.

by xevb3k 8y ago

lev99 8y ago

> The CaaS form already asks applicants for some personal information, such as LinkedIn profiles and educational background. Carroll is resistant to the idea of using such data to glean insights about businesses. Because well-educated white men have the easiest time raising money today, any model using demographics to predict success would favor them—the opposite of her intention. Still, Social Capital is experimenting with building personalized models anyway, though it hasn’t implemented any yet They specifically talk about this risk, current data models only use business data and they discuss the dangers of using personal data. It seems to me that data like customer loyalty and cash on hand are perfectly fine to use and don’t come with any direct gender or ethnicity bias.

xevb3k 8y ago
My point is that a number of features can be well correlated with gender/other social factors and result in training bias into the algorithm. The following article discusses this in the context of policing for example: https://boingboing.net/2015/12/02/racist-algorithms-how-big-dat.html https://boingboing.net/2015/12/02/racist-algorithms-how-big-...
- lev99 8y ago
  My point is the article doesn't ignore it, and are specifically targeting data points less likely to have gender/race bias. You presented your comment like it was a point of view not covered in the article.

nl 8y ago

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.’ ”

bsder 8y ago
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 8y ago
  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.
  - xevb3k 8y ago
    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.
- fwdpropaganda 8y ago
  > You learn to ignore the excuse and move on (...) That's actually a great attitude in almost everything in life: recognize that some things are best modeled as a random variable, and if you fail there might not be a causal explanation and you just have to keep trying. The downside is that you might be given valuable information (feedback) and you might dismiss it as noise. So it's a real skill telling signal from noise.

vasilipupkin 8y ago

the point that the algorithm is or isn't biased. the point is, humans are also training their experience on past deals, so the issue is, is the algorithm more biased, on average, than humans, or less? I have absolutely no doubt that an average VC fund decision making process can be replaced by an algorithm. Best vc funds - that's a tougher call. But in general, algorithmic investing is superior to seat of pants investing.

xevb3k 8y ago
If a human is biased, there are individuals whose attitude can be corrected, and if necessary pressure can be put on individuals to correct the situation (politically or otherwise). With an algorithm, you show the inputs, show the outputs and say “no bias, it’s all automated”. Look, part of the problem might be that at seed stage the optimal short term strategy might be to be biased. If series A,B investors are sexist or racist, then that’s going to make things harder for those companies to progress. An algorithm might pick up on that, particularly if it can find features correlated with sex, race, or some other factor that investors may be biased against. As to your point regarding the average VC. It’s possible that selecting companies at random might do better than the average VC. I suspect if we did, the societal outcomes might be better.

candiodari 8y ago

You forget the last source of bias : the bias could be on a lower level. For instance, there could be some sort of bias, hidden to the algorithm, that makes certain decisions succeed and others fail in a biased way, but for instance if we're talking companies, state-backed companies succeed, foreign companies suddenly fail. There could be a real world bias.