compbio

The "problem" here is not secure communication. It is media propaganda / information warfare. Facebook and Twitter being used to instill hate and spread conspiracies. It is all in the open: Facebook images stating that Israel is behind ISIS, or Twitter accounts that post nothing but Anwar al-Awlaki videos. Could you imagine that happening 10 years ago, on your own homepage, without being raided? If Twitter can block porn, surely they can block terrorist propaganda too. But the law enforcement probably want to use these for fishing. Instead Clinton wants to build another nuke.

> "Dad, what happens if Donald Trump wins and we have to move out of our homes?"

These are propaganda tactics close to character assassination. In the next sentence he says that "freedom of expression" unites us, but when Trump uses this freedom of expression he is suddenly scaring Muslim kids. Very recognizable.

> especially from someone as untried and as incompetent as Donald Trump.

You just know that they made this a talking point, a hook. And O'Malley wrestled it in his answer, because that is what he prepared.

> where do you draw the line between national security and personal security?

Donald Trump is incompetent. Next question! Next!

Of to Google "Are all The New York Times articles shitty journalism?" Our research shows that in the months after these searches, the number of shitty articles on The New York Times seems to rise. We know this before the FBI does so, because we wield the power of Google Trends.

It is, and has been, a far cry from actual optimization: Giving your content the best possible ranking it deserves.

Want to be able to rank for an exact sentence match? Sure, use Angular. Want to actually compete for rankings? Do not build a single-page Angular application.

Solid SEO is about optimizing for users, thinking: "would I do this if search engines did not exist?". SEO for Angular apps has become: "Detect and redirect one of the major search engines to a text alternative".

I feel that a different number of darts is salvageable for this logic, but having thought about this blog post some more, I feel bias is inherently non-compute-able. Our decision on how to compute influences our results.

What PG did for me was show that there is no Pascal's wager in statistics: All outcomes/data/measurements/views are equally likely. The view that the female variable alone is able to divide skill/start-up success is weak. The assumption of non-uniform points is weak. The assumption of no variance/unequal rankings is weak. The assumption that a non-random sample is significant is weak. The assumption that VC's are unbiased in their selection procedure is weak. The assumption that nature/environment favors skilled women is weak. The assumption that decisions of who to fund does not influence future applicants. The assumption that women are still selected for capability is weak. The assumption that women ignore nature/environment and keep focusing on start-up capability is weak. It is much more likely that any other thing happens. PG's alternative is certainly a sane one, but one of many.

Perhaps women perform better because, while VC offers the same chance to men and women, they are better at picking capable women than capable men. Bias in favor of capable women.

Perhaps women perform better because, they are naturally better than men.

Perhaps women perform better because, VC is biased against women, and only the strong survive.

Perhaps women perform better because, affirmative actions to remove the inequality in performance (perceived bias) actually increased our objective bias.

Perhaps women perform better because, VC is bad at picking capable women, so they pick incapable women, of which there happen to be a lot more.

Perhaps women perform better because, now the smart and capable women start to act like the mediocre ones (bad funding decisions influence actors looking for reward)

Perhaps women perform better because, nature is "biased" against older risk-averse, but available, men and, older, unavailable women who have children, and nature favors both young males (who have to compete with the old males) and females (who compete only among themselves).

Perhaps women perform better because, our sampling method was biased.

Perhaps women perform better because, our measurements were 5 years old and we are seeing an old static state of a highly complex dynamic system.

Perhaps women perform better because, they are more variant. The good ones are really good and the bad ones are really bad, making it easier on VC's to pick the cream of the crop.

All I know is how little I know. That (algorithmic) bias is an important subject, worth thinking about, and that we need very smart people working on this subject. I would never have gotten away with upvotes on my posts in this thread if the subject was cryptography. I clearly know very little about both subjects (and only now I know that, which I hope is at least a start).

PG showed that we (I), perhaps too easily, go along with the status quo: Our measurements are all correct, our conclusions are all correct. While, if you think about it.. objectively I agree that women and men are equal in capability. If you believe this to be so, then you may have a selection bias, if you observe that men and women perform differently.

I think the least all views could do is to make sure the environment for female founders to flourish is healthy and in line with skill/capability. Then let nature do its thing.

P.S.: If we know that females actually perform better than males, what is the ethical thing to do? Fund even more female founders and make it harder for men? It would make you richer. Affirmative action? It would not remove a bias, it would introduce one.

    "C" the applicants you're looking at have roughly 
    equal distribution of ability.
	

If we take two dart boards (one for female -, one for male founders) as a visual, where hitting near the bull's eye counts as "startup success".

If we take "C" to be true, then the darts would be thrown at random.

Now we draw a circle around the bull's eye. Anything landing in this circle we fund. If this circle has a smaller radius on the female dartboard, than on the male dartboard, then evidently the smaller female circle will contain more darts closer to the target (better average performance) than the larger radius male circle.

But then we do not even need performance numbers: Smaller radius circles will have less darts in them. Using "C" we only need to know that the male-female accept ratio is not 50%-50% for us to have found a bias.

In short: If you see a roughly equal distribution of ability, and (for simplicity) a roughly equal number of female to male fundraisers, then you should always have a roughly equal distribution of female to male founders in your portfolio, performance be damned.

The technique is still useful for when you do not have these female vs. male accept ratio's, and a VC publishes only success rates, but this information on ratio's is often more public than success rates/estimates.

Assumption: There is no fundamental difference between a female and a male founder for achieving start-up success (average rates and variance/distribution of rates is the same)

Observation: VC funded start-ups with female founders are (on average) 60% more successful than start-ups with male founders

Hypothesis: VC funding is biased against female founders. The ones that do receive funding are better vetted, less risky, and have higher individual qualities.

Experiment: Start funding more female founders.

If we then observe: The numbers start to even out, then there is no fundamental difference. VC funding bias may have been the cause of the difference in success rate.

If we then observe: The numbers stay the same, then there is a fundamental difference and our assumption is flawed.

Rational choice: Start funding more female founders. This either removes a bias (levels the playing field), or increases your profit (funding more potentially successful founders).

PG should of course not use an hypothesis to prove an assumption (experiment/probing is needed for verification). But also: The possibility of an uneven distribution should not invalidate such an experiment (or PG's line of reasoning), it will merely bring it to light (the numbers would stay the same, thus we have shown that the difference is fundamental and not caused by a sampling bias).