urish

tl;dr by Laura Norén (@digitalFlaneuse on twitter):

Stanford professor, Ilya Strebulaev, and Will Gornall of the University of British Columbia recalculated the valuation of 100+ companies known as unicorns (startups valued at $1bn +) and showed many aren't worth nearly as much as they claim. Why? Because math. Startups typically issue different classes of stock in each fundraising round but their valuations are oversimplified by applying the price of the most recent round to all outstanding shares. Every company they looked at was overvalued, 53 lost their $1bn unicorn status, and 13 were overvalued by more than 100 percent. ... "Some unicorns have made such generous promises to their preferred shareholders that their common shares are nearly worthless," the two professors wrote. In my opinion, this is an example of two things 1) lots of people cannot apply their math skillz and 2) the ethos of finance contains much magical thinking. The entire industry is obsessed with unicorns. According to Scottish myth, unicorns were ruthlessly hounded by clamoring hoards, simultaneously scapegoated for being the aberrant creatures they are and loved to death (e.g. abused, fatally) for their magical powers. Lesson: it's clear that many in finance are not good at applying their history and culture skillz, either.

My only disagreement is with: >Human processes just add additional bias

Bias with respect to what? As you say, there is already bias baked into the data collection and the algorithmic choices.

The bias that human editors introduce is different, but not necessarily larger, however you even measure it. There are also myriad human choices behind the choice and deployment details of the algorithm.

An important plus for human editors is greater interpretability and greater transparency regarding the biases the system ends up showing.

Weird that the filter didn't catch it - it seems to be exactly the same URL in both cases.

I wonder at what point does using fewer words become detrimental to understanding.