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