richmarr

> If its wrapper is open, assume it is used. You can only do that safely with single-use items.

How about putting the re-usable item in a wrapper so that the same rule can apply.

> These tech companies aren't direct buyers any more than you or me

Well, they're (a) further up the supply chain that we are, and (b) have the resources to understand and influence their supply chain. You can be pedantic about the word "direct" if you like but I don't think that's useful.

Information, and control.

All of the products I can buy may or may not contain this unthetical cobalt. I don't know which, and my personal buying choice doesn't effect anything.

What are you proposing, that everyone with a smartphone or a computer be sued? How will that work?

If you offer them a better margin to mine in a more ethically palettable way, and any up-front resources to do so, then it's reasonable to assume that they will.

I think this quickly gets into the details though. How much safety is required and what does it cost? Are there alternative materials that cost less than ethical cobalt? What age restrictions should be put on the labour involved and what will those children do instead (both with their time and to earn money)? Where will the adult workers come from to replace those kids and what training do they need?

Because the direct buyer is the one who has the most information and the most control.

In contrast the end user has almost no information, so punishing them is both unfair and ineffective.

> I will say though, the problem is one of “standardization” across an organization where it’s too big for everyone to fit in a room.

I think you've got a lot of this right (disclaimer: we've built the product I think you're describing)

The don't think the most important problem is standardisation though, it's observability/instrumentation ie. if you don't measure what's working, you can't improve things.

The very best tech companies measure quite a lot, and often look back at their hiring processes in the event of a mis-hire to figure out what went wrong and how they can avoid the same happening in future... but even then they only do that in exceptional cases because it's done fairly manually. That means they have low statistical significance and a stuttering cycle of learning.

I believe they should be constantly looking at what's working well, for every hire. So that's what we built.

Once your hiring pipeline is trivially visible, a lot of these questions go away. You can see what's working well and try new things in safety, you can optimise with your eyes wide open.

One thing we did straight away was to deprioritise CVs and replace them with written scenario-based questions relevant to the job. If managed properly that takes your sift stage from a predictive power around r=0.3 to a performance we find typically above r=0.6. Far fewer early false negatives makes your hiring funnel (a) less leaky, (b) more open to pools of talent previously ruled out by clumsy CV sifting, and (c) potentially shorter as the improved sift accuracy allows companies to consider dropping their phone interview stage(s)

Our NPS rating for HR teams is currently running at 85, and MRR churn is under 1% so there's clearly some value to the approach.

If you have a point, just make it.

In a world where the median per capita income is $2,920 per year this just pushes us closer to the plot of Elysium.

(Gallup 2013)

> Later during a calibration, the signals and the evidences are presented to the interviewing peer group (recruiter, hiring managers, interviewers from other rounds), and pretty much disallows for any unconscious bias such as "I don't think Alice would be a good team lead (because she is a woman, and woman are not good managers), or "We should not hire Amit (because he is an Indian, and Indians write poor code").

You've explained that your interview process has a predetermined scoring system which is a good start. I'm curious what the effect of this calibration stage is... did your company do predictivity and bias analysis on it?

If I understand you correctly I think this is misleading.

Discussing candidates after an interview allows social dynamics within the group to distort the signal so you reduce the value of taking independent data points. Not only will it not reduce bias in the way you seem to suggest, but you'll also lose some of your ability to reduce random noise as the noise from more dominant interviewers will be amplified.

I don't have time to dig out citations, but a good starting point would be "What Works - Gender Equality By Design" by Iris Bohnet. She's one of the world's leading academics studying how biases are affected by different hiring techniques.

So your argument is that they should be above examination of their interview process because their investments are doing well? Come on, you're just arguing for the sake of it now.

Multiple independent assessments are great at reducing random noise. Bias is noise, sure, but it's by definition not random so you need other forms of intervention to counter it.

> They are much less likely to be similarly biased against irrelevant factors like accents, mannerisms, backgrounds, etc.

They're not less biased, they just average out their biases over the group.

Your assumption is that three people chosen from a fairly homogenous pool are going to cancel out each others biases, which is... optimistic.

I don't know from this conversation what they're actually doing, but what they should be doing is using a diverse set of opinions to create a fixed set of questions and a fixed marking scheme, and then sticking to it for that round of interviews. Then looking back over time at every interview question and analysing how well it predicted later outcomes.

So we're agreed that "top 1%" and "upper class" are not interchangable terms.

> You can still avoid most of the effects of a worst-case bias by adding two additional measurements... given 3 who all had the same experience

You're right that it's an advantage, but it reduces noise, not bias.

Bias by definition skews systemically in the same direction so the positive effect of taking multiple measurements is minimal.

Presumably there's more to this than comes across in your comment.

After all, you don't avoid the unconscious bias of a single mind by adding more minds. That just gives you three sets of unconscious bias and adds biases caused by group dynamics.

Do you have a link? I may be googling the wrong terms.

> The threshhold for being in the top 1% of income is that 99% or more of people make less than you.

Clearly I mean the threshold for being upper class a term mentioned in your actual comment... unlike "top 1%" which was not.

Thanks a bunch.

Where should that threshold be? Does it move every year due to inflation?

I think a more reliable distinction would be whether a person's income predominantly comes from their labour, or whether it proedominantly comes from what they own.

> That’s a weird characterization, given that the trend has been in the opposite direction:...

I don't think you've adequately supported that criticism.

The article you're citing discusses the "top 4%" of income earners. That's a very different thing to "billionaires" of which there are 607 [Edit: in the USA].

Autocratic countries like... ones that ignore and undermine their own judicial system and appoint family members to positions of power?

This post discusses 'terminological precision' and tries to clarify 'mischaracterisations', which is fair enough, but fails its own litmus test by using terms like 'hate fest' and 'witch hunt'.

> I personally am concerned about my impact on the environment, but I consistently vote against collective action because articles like this make me really afraid of how crazy people are and what nonsense they'll believe.

On what basis do you think your vote will change other peoples beliefs?

Vote differently please.

I don't know what their actual budget looks like, but I do know that they're huge and have been making significant changes around digital transformation. That would result in high spending followed by later savings.

> The two are indistinguishable, unless...

So are they indistinguishable or not? Two things can't be a bit indistinguishable.

How would your hypothetical defence mechanism tell which C-suite initiatives are worthwhile, and which are ego driven make-work?

> ...at no extra cost

Complexity and dependency are costs too, and I would argue often more significant costs than the $30/month to run a server.

What threshold of proof are you expecting?

It may not be a lie, but it is argument ad hominem, so you can probably do better

There's a startup in London called Synthesized working on part of this problem space.

Given a source dataset they create a synthetic dataset that has the same statistical properties (as defined at the point the synthetic dataset is created).

I've seen a demo, it's pretty slick https://synthesized.io/

We're hiring two developers, ideally ONSITE in London. We can be flexible once we all know each other. We work primarily in Node and Vue.

Apply here: https://app.beapplied.com/apply/vcariijrr6

Applied is a company on a mission - we span out of the Uk behavioural science "nudge unit" are are passionate about using science to help teams hire the best person for the job regardless of race, gender or whether your dad plays golf with the CEO on weekends.

We’re looking for two software devs who are user-driven, and creative and want to help us leave the world just a little better than they found it.

We recently closed a seed investment of £1.5m, and have already helped hire over 2,000 people... over half of whom (according to our data) would have been normally overlooked.