Idontknowmyuser

what did he say worse than that?

I feel like this is bullshit. It's very clear that the photo is inspired by the cat, not the photo of the cat. There are many different details between the two images to the point that it is a totally different photo of the cat ( see ear position,left eye ..)

a limit to how hot something can get get, doesn't imply that something shouldn't be hot.

wait so a correlation between brain size and "generral intelligence score (g)" is phrenology? don't you feel like your mispresenting the study?

do you have any proof of this claims? and if they were true how and in what you way would this matter if we were to compare one individual to one individual instead of treating people like herds.

freedom of association is incidental to my initial response. I was speaking about freedom of speech they are different things.

by check if the point values are reasonable

I meant that the amount of points is not a major outlier, and if it is the interviewer should have written an argument why it's different from the old cases if the other members find the argument convincing it should be fine.

>some Applicant Tracking Systems like Greenhouse

that's exaclty the type of feedback I wanted thank you very much

> Claim E

if we are innocent, why wouldn't this be in our favour.

that was actually a point in his memo.

"Women on average show a higher interest in people and men in things ○ We can make software engineering more people-oriented with pair programming and more collaboration. Unfortunately, there may be limits to how people-oriented certain roles at Google can be and we shouldn't deceive ourselves or students into thinking otherwise (some of our programs to get female students into coding might be doing this).

● Women on average are more cooperative ○ Allow those exhibiting cooperative behavior to thrive. Recent updates to Perf may be doing this to an extent, but maybe there's more we can do. ○ This doesn't mean that we should remove all competitiveness from Google. Competitiveness and self reliance can be valuable traits and we shouldn't necessarily disadvantage those that have them, like what's been done in education."

The idea is that instinct has in itself an unconscious bias. By making the reasoning part conscious we hope to eliminate some of it.

so let me get this staright: when Damore is being rated politics have an effect on ratings and promotions

when Damore speaks out against politics affecting hiring and promotion, he is wrong

I'm fascinated how one can reconcile both beliefs

2/2

Step 2: peer-review The highest candidate is selected, interviewers exchange notes and are asked to:

* check and discuss if their colleagues reasons’ are valid and reasonable (for example "the hello" reason might be seen as too petty by some, and will be discarded)

check if the point values are reasonable (Checking old similar data to flag outliers is a possibility)

check if some of the reasons presented by other colleagues apply to their own candidates. For example, if the "hello" reason was not discarded and your candidate didn't say hello you must apply it to him too.

*Interviewers are asked to bring more focus on the current best candidate's reasons.

If after a peer-review round, the best candidate changes, another round is made or shorten the list and re-interview.

If it does not change, the best candidate is offered the position.

Claim D: this helps smooth out harsh or lenient interviewer bias.

Claim E: this process if well documented should be a valid and easy defense against allegations.

Data Analysis on hiring decisions becomes way more interesting. I'm sure there are tons of trends you can seek out.

Claim F: this allows an earlier detection of discrimination. For example if you find that an interviewer or a committee always removes points from a certain group for a subjective reason more than others in the company. It might be an early warning sign that a problem need be addressed.

Claim G: this allows for a higher quality debate on controversial issues like sexism and racism in hiring.

Machine learning can be used to give suggestions to interviewers about the amount of points to give for reasons. (They should be suggestions no decisions to avoid the pitfalls the lost nuance that classic machine based decision might suffer from)

Example: a fairly easy one is that after a high enough number of universities and GPAs is received one can aggregate the point values into scores that take into account the difference of grading and quality between universities. This might help decide if GPA x in Y is better or worse than GPA z in C, a very hard question to answer fairly if we didn't have the data.

I think this method offers a more traceable, open and perhaps fair way of hiring without suffering from the lack of nuance that traditional automatic hiring suffers from.

Problems:

- Money: this process might require more man power.

- Tooling: to be efficient this process requires tooling and automation

I would be happy to hear your ideas, improvements and experiences with similar systems. I think the idea of this system is very similar to that of a neural network.