A venture fund's experiment in human-free investing

> 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.

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.’ ”

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.

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.

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.

I'll defend pseudo-systematic, for the sake of it if nothing else :)

Investment types with many rows of data, enabling truly systemic decision making are securities markets. Whether you are using ML, or a human analyst with a theory, the economic conclusion is the same. A securities market has lots of data. This lets traders price systemically. Pricing, investing & trading are the same thing, in a securities market.

Startup investing is not like that, generally. A person using their subjective faculties is heavily involved, biases and all. This exists because human's subjective cognitive abilities are not just delusion, they are a real cognitive ability even if flawed^.

Systemic & non-systemic systems have their strengths and weaknesses. Human biases are a big weakness for the systemic side. "Searching where the lights (data) are" is the big systemic one. A pseudo-systemic system is basically just a compromise. If the weaknesses of a non-systemic system are a major problem (the whole premise here is that it is), then it is not a stupid idea necessarily.

It doesn't even have to be all that sophisticated. Manual overides are not necessarily a bad system. They make it clear where subjective judgement was used, how often. At least you are aware that it has taken place.

I think the blind orchestra is a good metaphor. It identifies the kind of human bias you are trying to minimize. If one orchestra looks like a Viennese orchestra and the other looks like a hillbilly high school, you want to hide this from human judges. You want human judges to focus their subjective brains elsewhere. You don't want to create an objective test of "good music" and "bad music," because the definition you would be forced to use would suck. At least, the definition would be different, like a sport with rules. So, compromise.

I don't think it's impossible to think of pseudo-systemic investment strategies, based on a similar compromise.

^ For best results, use skin in the game.