Amazon scraps secret AI recruiting tool that showed bias against women - <AUTHOR> https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G <comment><USER2> The eye opening thing here is not that the AI failed, but why it failed.

At start the AI is like a baby, it doesn't know anything or have any opinions. By teaching it using a set of data, in this case a set of resumes and the outcome then it can form an opinion.

The AI becoming biased tells that the "teacher" was biased also. So actually Amazon's recruiting process seems to be a mess with the technical skills on the resume amounting to zilch, gender and the aggressiveness of the resume's language being the most important (because that's how the human recruiters actually hired people when someone put a resume).

The number of women and men in the data set shouldn't matter (algorithms learn that even if there was 1 woman, if she was hired then it will be positive about future woman candidates). What matters is the rejection rate which it learned from the data.. The hiring process is inherently biased against women.

Technically one could say that the AI was successful because it emulated the current Amazon hiring status.

<comment><USER3> The article didn't specify how they labeled resumes for training. You're assuming that it was based on whether or not the candidate was hire. Nobody with an iota of experience in machine learning would do something like that. (For obvious reasons: you can't tell from your data whether people you did not hire were truly bad.)

A far more reasonable way would be to take resumes of people who were hired and train the model based on their performance. For example, you could rate resumes of people who promptly quit or got fired as less attractive than resumes of people who stayed with the company for a long time. You could also factor in performance reviews.

It is entirely possible that such model would search for people who aren't usually preferred. E.g. if your recruiters are biased against Ph.D.'s, but you have _some_ Ph.D.'s and they're highly productive, the algorithm could pick this up and rate Ph.D. resumes higher.

Now, you still wouldn't know anything about people whom you didn't hire. This means there is some possibility your employees are not representative of general population and your model would be biased because of _that_.

Let's say your recruiters _are_ biased against Ph.D.'s and so they undergo extra scrutiny. You only hire candidates with a doctoral degree if they are amazing. This means within your company a doctoral degree is a good predictor of success, but in the world at large it could be a bad criteria to use.

<comment><USER4> Men are promoted quicker, and more often, than women.

<comment><USER5> There was a company meeting one year at Amazon when they proudly announced that men and women were paid within 1-2% of each other for the same roles. It completely missed the point which you raise.

I want to see reports of average tenure and time between promotions by gender. I suspect that the reason we don't see those published is that the numbers are damning.

<comment><USER6> Or possibly noone did a study of sufficient size that passed peer review.

It's also not hard to make the pay gap 1-2% just like it's not hard to make it 25% (both values are valid). Statistics is a fun field. Don't trust statistics you didn't fake yourself.

Amazon could easily cook the numbers to get to 1-2%, I doubt anyone checked the process of determining that number if it's unbiased and fair and accounts for other factors or not.

</comment></comment></comment></comment></comment><comment><USER7> Hold on here. This article seems to have buried a pretty important piece of information wayyy down in the middle of the text.

> Gender bias was not the only issue. Problems with the data that underpinned > the models' judgments meant that unqualified candidates were often > recommended for all manner of jobs, the people said. With the technology > returning results almost at random, Amazon shut down the project, they said.

Granted an article isn't going to get as much attention without an attractive headline but that seems a far more likely reason to have an AI based recruiting recommendation scrapped. The discovery of a negative weight associated with "women's" or graduates of two unnamed women's colleges is notable but if it's tossing out results "almost at random" then...well there seems to be bigger problems?

<comment><USER8> The media is no longer reporting things. You can't make money with reporting. The media is actively creating narratives, and one of the narratives that people are fed nowadays is that women are victims.

Men and women are pitted against each other.

Due to the way the media has evolved people consume their own biases and most often just read the headlines.

<comment><USER7> I mean, yeah you're not wrong. I try not to be too cynical about the whole thing even if I think the narrative is suspect. Yes women and minority representation in tech is a potential issue but I really want to know more about the AI recommendation system for potential hires. Especially if it was giving out spurious recommendations.

It's amazon, I can't imagine how many millions went into something like that. We'll almost certainly not get a postmortem but it's definitely intriguing.

</comment></comment></comment><comment><USER9> (Disclaimer: I am an Amazon employee sharing his own experience, but do not speak in any official capacity for Amazon. I don't know anything about the system mentioned in this article.)

I am a frequent interviewer for engineering roles at Amazon. As part of the interview training and other forums, we often discuss the importance of removing bias, looking out for unconscious bias, and so on. The recruiters I know at Amazon all take reaching out to historically under-represented groups seriously.

I don't know anything about the system described in the article (even that we had such a system), but if it was introducing bias I'm glad it's being shelved. Hopefully this article doesn't discourage people from applying to work at Amazon - I've found it a good place to work.

To say something about the AI/ML aspect of the article: I think as engineers our instinct is "Here's some data that's been classified for me, I can use ML/AI on it!" without thinking through all that follows, including doing quality assurance. I think a lot of focus in ML (at least in what I've read) has been on generating models, and not nearly enough focus has been on generating models that interpretable (i.e., give a reason along with a classification).

<comment><USER10> It seems like they did think it through, though? And that's why it's being shelved. I don't really see what the story is here. It seems like the whole process worked exactly as it should - Amazon tried something, it had some unintended consequences, they caught it, and shelved it.

<comment><USER11> Agreed. There is no story here.

<comment><USER12> The story is, some ML researchers did their job properly and detected ethical issues before they became a problem. That's more rare than you'd think.

<comment><USER13> Yes. Software engineers taking ethics seriously and not letting technical enthusiasm blind them is news, not normality.

</comment></comment></comment></comment></comment><comment><USER14> This will be unpopular but I don't care. What is the evidence that the source data for this 'AI' is biased because the men it came from did not want to hire women? Is there a reserve of unemployed non-male engineers out there? If so what evidence is there of that?

Technical talent is both expensive and a rare commodity for tech companies. The non-male engineers I've worked with have always been exceedingly competent, smart, and their differing perspectives invaluable. If there was an untapped market of engineers you'd better believe every tech company would be taking advantage of it.

<comment><USER15> Oh, there are a bunch of us, even here in the SF Bay Area. Trouble is, we're older than 35, or don't have degrees from "top" schools, and/or don't have the "passion" for bizarre extended hiring rituals. I could staff an entire dev team with non-male people within a week.

<comment><USER16> So what do you do now? Btw some men in tech are also over 35 and tired of hiring rituals.

<comment><USER15> After 15 years of front-end dev, I now work in retail. Some of my other peers are scraping by with Uber/Lyft. Some are muddling through as housewives or substitute teaching.

And, yes, Bay Area tech hiring is needlessly hostile for men over a certain age as well.

<comment><USER16> Ageism, OK - but still I find it hard to believe that you can't find a job if you can code. Maybe competition or demands are especially high in the Bay area?

</comment></comment></comment></comment></comment><comment><USER17> This is a direct and clear example of bias which made it easy to flag the ML algorithm. But what about ML algorithms that are inducing benefits to groups in less obvious contexts? What about groups that are not so easily identified as being protected classes by simple, human-understandable model features? What about cases where the features are just merely correlated with a subpopulation of a protected class?

If we're being honest, a system only needs to be in a decision-making _capacity_ for discriminatory behavior to be scrutinized, since in many cases human operators will not be able to identify the specific features being used to make decisions about people -- the features could be highly correlated with some subpopulation of protected class. If you take that to be true, the question reduces onto what decision-making roles ML algorithms have that could be discriminatory, and it's hard to argue this is not a massive part of their current and expected roles.

I think this is going to be a long, winding ethical nightmare that is probably just getting started by human-digestible examples such as these. One can imagine things like this one being looked back on as quaint in the naivety to which we assume we can understand these systems. Where do we draw the line, and how much control do we give up to an optimization function? Surely there is a balance -- how do we categorize and made good decisions around this?

As far as I know, a cohesive ethical framework around this is pretty much non- existent -- the current regime is simply "someone speaks up when something absurdly and overtly bad happens."

<comment><USER18> _> What about cases where the features are just merely correlated with a subpopulation of a protected class?_

This question can be rephrased as "is there a difference between de facto and de jure discrimination?"

My answer is no, causality doesn't matter here: if feature A is a good predictor that some person belongs in group B and not group C, then filtering out feature As is effectively the same as filtering out only group Bs.

<comment><USER10> Ok, so if you're hiring professional arm wrestlers, and your model looks at bicep muscle mass, is that discrimination because it selects against women?

If you're hiring therapists, and your candidates take a personality test, and your ML model weights the 'nurturing' feature highly, is that discrimination because it selects against men?

<comment><USER18> Underlying your examples is the implication that a preference shouldn't be considered discriminatory if the trait being selected for correlates with fitness. I agree with this position!

What I don't agree with is the assumption that, in this case, the preferred traits _do_ correlate with fitness, since there's at least one — gender — for which this model is biased even though it has no apparent correlation.

<comment><USER10> Ya, I just mean to say that uncorrelation with fitness is an important qualifier.

</comment></comment></comment></comment></comment><comment><USER19> At best ai amplifies existing patterns and biases when handling repetitive work. Over and over we hear how Facebook, Twitter, Google, and others will solve the problem of problematic content and bad actors through ai and neural networks. It's a fraud and the digital potemkin village of our era.

<comment><USER20> AI learns from the training data it's given and copies any biases this data exhibits. Pretty much all software today uses ML in some form to improve their services. I feel it's here to stay and not bad by default. We just have to make sure we are aware of its current limitations.

Facebook is already auto-flagging content this way but it's just a very hard problem (even for humans).

<comment><USER21> _AI learns from the training data it 's given and copies any biases this data exhibits_

I hate to sound like "that pedantic guy", but I'd argue that the quote above is only partially true. It's the case that _some subset_ of AI techniques "learn from the training data it's given and copies any biases this data exhibits". There are AI techniques that aren't based on supervised learning from a pre-existing training set. That doesn't mean that those techniques can't wind up adopting the biases of their human overlords, but I believe some aspects of AI are _less_ susceptible to this kind of bias, than others.

</comment></comment></comment><comment><USER22> Call me cynical but I found it amusing that no one points out the fact that engineering work is laborious and dry to say the least for most people. That's the reason why there are so few people who have other options, females, upper middle class people, people of means, in the scene. There are so many lowish paid low status unsought for sectors where majority workers are male, say janitors in Universities, why I never see any discussions on that bias ?

<comment><USER23> You're saying that software engineers are in the role because they have no other option? No upper-middle-class people in the position? What are you talking about?

You're missing the point anyway. The article made it pretty clear that this AI amplified biases humans already have about women applicants to tech positions. Stating your own biases about women make no sense to the topic, or to the argument you seem to be trying to make.

Janitors are worth talking about as well (women in the same job usually have a different title with less authority and less pay), but high-status, highly- paid, highly influential jobs are where it's most important to avoid bias, and so we talk about those more.

<comment><USER22> And of you think programming is high status, high paying, highly influential, why we import immigrants to do that ? You think the talent pool of some hundred million American people with most entensive education system is insufficient ?

</comment></comment></comment><comment><USER24> The explanation seems overly simplistic. If the difference in volume of male candidates mattered, then I would also expect to see a bias in favor of applicants from larger universities. That seems like too obvious an issue in the way the algorithm was designed.

I see four possibilities here:

1\. The algorithm was designed in a completely inept fashion

2\. The algorithm design was sound, but ultimately ineffective

3\. The algorithm was sound and effective, but results were considered discriminatory.

4\. There's something biased about how employees are rated--the data that would feed into the algorithm, which is possibly more of a human element.

Edit: Added fourth possibility

<comment><USER25> We're talking about Amazon, one of the biggest powerhouse ML employers. I don't buy that the model was poorly designed or ineffective. They also didn't just scrap the model without understanding how or why it failed to meet its objectives.

And whatever the cause was, it was not the poor quality of the training data. They tried to stop the model from downranking women based on obvious keywords, only to find it learning to downrank them based on more subtle language cues:

> Amazon edited the programs to make them neutral to these particular terms. > But that was no guarantee that the machines would not devise other ways of > sorting candidates that could prove discriminatory, the people said.

So the answer is 3 or 4.

If the answer was 4 then they would have probably mentioned the cause of the bias somewhere in that otherwise detailed article. But they didn't, possibly because the cause is controversial - probably option 3 but possibly still option 4.

And then there's the subtle cop-out:

> Gender bias was not the only issue. Problems with the data that underpinned > the models' judgments meant that unqualified candidates were often > recommended for all manner of jobs, the people said. With the technology > returning results almost at random, Amazon shut down the project, they said.

If the model was actually useless and returning random noise, then there wouldn't be any bias, and the article wouldn't need to talk about discrimination. This paragraph reads to me like they decided to mention long- tail results (that you'd find in any ML model) as supportive 'evidence' that the model was somehow broken rather than producing valid but controversial results.

<comment><USER26> Well it's also what you're training for. If you're building a machine to return "John Doe, The Company's Best AI Dev" then there's a few things that you might get back from a working and effective machine. The problem is that while the machine is doing the best to replicate a John Doe, the humans who designed the machine might realize that there's a lot of variables in what they're looking for that scoping the design is impossibly complex.

Basically, people WANT bias, but they want specific bias. One of the difficulties in training a machine to understand what you find as viable bias vs problematic bias is all the tiny nuances. Yes, you want a great engineer on paper, but you also need to have as diverse a cast as you can in your company (both for optics and creative solutions) AND you need to get people you can afford AND you need someone who's enjoyable to work with etc etc.

Hiring is always going to be part art and part science. There will always be some type of discrimination because of the perceptions of what makes a good qualification for the job. Any hiring group is just going to have their own hierarchy of what they think are the most important skills to have. You can only approach perfection/unbiased hiring, you can never actually achieve it.

</comment></comment></comment><comment><USER27> I wish we could move away from resumes for tech role screening anyway, since they convey very little real reliable information. I've seen too many great hires from candidates with relatively weak resumes, and failed interviews from candidates with great resumes (and obviously vice versa).

I'm not sure what the best alternative should be, though. I am a fan of open source work as a sort of code portfolio, but it doesn't work for every kind of engineering/science (edit: and also would introduce bias against professionals too busy for open source.)

Regarding bias — it seems the only way to truly eliminate it (including unconscious bias) is author-blind reviews, i.e. reviewing code written by a candidate without knowing anything about that candidate's identity. (And the nice thing about code is it usually doesn't signal any identity traits of the author via side channels.)

<comment><USER28> > author-blind reviews

Should I ever be in a position to hire a colleague, I wouldn't ever do so without having a chat with them.

I spend 8hrs a day in an office with my colleagues (sometimes more than with my wife & kid) and the ones I can't stand is about the only thing wrong with my job.

If we can't even see the person's face over some gender bias hysteria then I wonder how the hell we got here.

People should just get over the fact that men and women are different.

<comment><USER27> I'm not saying that all candidates should be interviewed without ever meeting or interviewing the candidate face-to-face; rather, I was speaking specifically of the coding interview portions. In these segments, face-to-face doesn't really matter IMO, since it's all about the candidate's problem- solving ability.

Yes there's a lot of "bias hysteria" out there, as you put it, but I would dispute that advocating "author-blind meritocracy" falls into that category.

Quite the contrary: An author-blind review process would actually make any bias impossible — either for or against any particular identity group. It seems to me most people should be able to get behind that, but maybe I'm wrong.

In fact, the main opposition to author-blind meritocracy is the "post- meritocracy" movement which is slowly making its way into open-source projects codes of conduct.

<comment><USER28> You're totally right in that coding tests can and probably should be done "blind".

When it gets to the "let's meet" stage, it could be possible that the bias just comes back. Yes this woman made a perfect score on the coding but she's a woman and I'm not going to hire her for reason X that just bubbled up out of my biased brain. I can totally see that happening, unfortunately.

</comment></comment></comment></comment><comment><USER29> The idea that Amazon is trying to enforce diversity by using an algorithm that is made to detect, and match, patterns boggles the mind. Why, yes, if your recruiting cost function is "is that person just like all the others we hired", you will end-up with a non-diverse workforce, no matter whether the model optimized with this function has 20 layers, 250 hyperparameters, or two legs, two arms and a fast-receding hairline.

You can de-bias by explicitly controlling for gender, but now everyone in your company went to CMU and likes dogs.

The more I see news about what recruiting for ultra-large corporations, the more I think one of two things is true:

* ultra-large corporations are doomed to hire less and less well in a way that is more and more biaised, and we should regulate against such corporations in a way that forces them to redistribute their wealth to SMBs;

* ultra-large corporations need to start exclusively growing through acquisitions, which will have the effect of redistributing their wealth to SMBs, and also of hiring a more diverse base of employees because there is a priori a greater diversity of backgrounds leading to success in the free market than the diversity of backgrounds leading to success in the Amazon interview.

</comment><comment><USER30> Anyone who has worked at tech companies and has been involved in the hiring process knows the following:

The best thing to be right now is a woman engineer. You can easily get hired within the week.

Unfortunately this doesn't seem to be well known outside of those involved in hiring.

<comment><USER31> Is this anecdotal? Are you insinuating that women have an upper hand now for reasons beyond their skillset?

<comment><USER30> Yes. There is a bonus to being a woman in this situation, all other things being equal.

Silicon Valley did a good bit on this [https://www.youtube.com/watch?v=Dek5HtNdIHY](https://www.youtube.com/watch?v=Dek5HtNdIHY)

It's funny because it's true.

</comment></comment></comment><comment><USER32> Garbage in, garbage out.

The system failed because they were trying to solve the wrong problem, or maybe more specifically, didn't solve the problem that led to the problems with the AI. Amazon was treating the hiring problem as an _efficiency_ problem alone, and ignoring the _bias_ problem. So they wound up training the AI to do a shitty job much faster than humans ever could be shitty - and, by analyzing the data in a way the human results weren't analyzed, showed the failings of the human hiring process.

Existing process is sexist. Automate to "improve" it, and you wind up with something even more sexist. What this means is that Amazon needs to go back and revamp their whole hiring process to make it fair, before trying to make it faster.

<comment><USER33> > So they wound up training the AI to do a shitty job much faster than humans > ever could be shitty

If nothing else, modeling our existing behavior in this way is a great use case for ML. As it allows us to "fast forward" and thus--hopefully!--identify our flaws based on modeled iterations.

</comment></comment><comment><USER34> I would guess that the training data for the ML set was the set of all resumes and an indicator of whether the candidate was eventually hired (maybe with supplemental data about how far in the process the candidate got).

Could this be a direct indicator of a powerful subconscious bias in Amazon's existing hiring process?

<comment><USER35>
      Could this be a direct indicator of a powerful
      subconscious bias in Amazon's existing hiring
      process?

Maybe - but maybe not.

Imagine a company with 2 men in HR, 2 women in HR, 40 men in engineering, and 10 women in engineering. That's with gender-blind hiring, reflecting only the 4:1 ratio of male to female CS graduates.

If you picked a random male hire, there's a 40/42=95% chance they're an engineer whereas if you picked a random female hire, there's a 10/12=83% chance they're an engineer.

Thus if you look over all hires' CVs, due to Bayes' law the dataset says being male increases the conditional probability you meet engineering hiring requirements - and the ML system picks up on that.

<comment><USER36> Why would one train on "all hires CVs"? It'd be "engineering CVs", moreover it'd be "engineering applicants CVs", not "engineering hires CVs".

<comment><USER37> In the article I noticed: _" Problems with the data that underpinned the models' judgments meant that unqualified candidates were often recommended for all manner of jobs"_

That language is a bit ambiguous, it could just mean that the algorithm failed on a wide variety of jobs beyond engineering. But another reading suggests that the algorithm was not asked "is this person a good fit for this role" but instead "what, if anything, is this person qualified for?"

If that's the case, then the problem starts to make more sense: the algorithm learned a correlation between male-sounding resumes and being hired for engineering roles. That could produce a biased approach even if the decisions in the training data were gender neutral but position-specific. Of course, it would also mean that an Amazon ML team trained an algorithm with inputs that didn't match to its eventual task, and makes me wonder what they used as a test set...

(Anecdotally, Amazon spent quite a while recruiting me for SysEng work I'm wildly unqualified for and uninterested in, even suggesting a switch to applying for that team when I was already in the funnel for something I'm more qualified at. When my resume eventually made it to a syseng engineer, they were rightly baffled that I had landed on in their stack, giving me the sense that _something_ was screwy with how Amazon decides who heads towards which role.)

</comment></comment></comment></comment><comment><USER38> I'm reminded of why Watson failed, and the problem with ml and ai in general- you can't peek under the hood to see why something happened, or how to keep it from happening without a lot of time, a lot of hard work, and a whole lot of carefully groomed data.

<comment><USER39> This is especially memorable/well treated in the Cory Doctorow novella/short story "Human Readable"

[https://craphound.com/stories/2005/10/12/human- readable/](https://craphound.com/stories/2005/10/12/human-readable/)

</comment></comment><comment><USER40> The problem is you cant feed the ML algorithm training data based on what your company currently looks like, you have to feed it an idealized set of what you want it to look like. It almost needs to be fictitious training data to hide the ugly bias that's already built in.

I don't think this will ever work. There is too much variability in resume wording that correlates to gender and even culture of origin even when you take out names and any other protected class identifying markers. The Dutch tried this and ended up with less diversity.

I'm going to go out on a limb and say you almost want to leave all that identifying data in, but put each candidate into buckets with separate rating algorithms trained against only that "type" of candidate. The top candidates from each culture, and the top candidates from each gender, etc etc, however you want to do it. Feed them into a picking algorithm that builds a composite of what you want your team to look like diversity wise based on the top candidates from each bucket, and go from there.

Don't take my opinion seriously, I'm not an ML guy.

</comment><comment><USER41> It seems that the only socially acceptable output for the AI would have been hiring women 50% or more and hiring minorities at a rate greater than or equal to their representation in the populations. Anything else is clear bias and discrimination.

The project was doomed from the start.

</comment><comment><USER42> Machine learning should not be used in this way on humans, whether for resume screening or even more dystopic, for sentencing.

<comment><USER43> Why not?

Seriously: let us take as given that the AI models are biased. Will you also admit that the existing processes are biased? If so, then what we need to ask is which is MORE biased. It might be complaining that we shouldn't release self-driving cars because on rare occasions they cause accidents.

There is, however, another criterion besides how biased it is: how biased it will be in the future. Human-driven processes have the opportunity to become less biased in the future (also the chance to become more biased, but overall things tend to improve). AI processes that are opaque might lock in bias in a fashion that is unreviewable. I believe that the solution is to build AI models that are more transparent -- that could be BETTER (in terms of avoiding bias) than the human-driven processes we use today.

<comment><USER42> I think we basically have the same view. I don't support black box machine learning models. I do support using automated tests and simple well defined objective criteria though, which is basically a transparent AI model.

It's just that generally what seems to distinguish whether something is called "machine learning" rather than "data science and modelling" is that the former is black box and the latter is not.

</comment></comment></comment><comment><USER44> I tried to do something similar a while ago (for eng hiring specifically). It turned out that the number of grammatical errors and typos mattered way more than anything else on a resume.

[http://blog.alinelerner.com/lessons-from-a-years-worth-of- hi...](http://blog.alinelerner.com/lessons-from-a-years-worth-of-hiring-data/)

That aside, what sucks is that attempts to automate resume scoring rarely look at harder-to-quantify features and focus on low-hanging fruit like keyword occurrences... though in my experience it's such a low-signal document for engineering hiring that the whole thing is a fool's errand.

</comment><comment><USER45> This is not very surprising - Machine Learning algorithms trained on biased datasets tend to pick up the hidden biases in the training data. It's important that we be transparent about the training data that we are using, and are looking for hidden biases in it, otherwise we are building biased systems. Fortunately, there are open source tools out there that help audit machine learning models for bias, such as Audit AI, released by pymetrics - [https://github.com/pymetrics/audit-ai](https://github.com/pymetrics/audit-ai)

</comment><comment><USER46> I hate this industry. Shooting themselves in the foot over and over again because no one can get passed the idea that possibly, women can be just as good at math, logic and computer science - if people would just let them. This never ends. It's just one place after another, when it gets discovered. It never changes.

<comment><USER47> Why not "women are just not as interested in math, logic and computer science to pursue it AS OFTEN as men"? Why are you not considering this possibility?

<comment><USER48> Ah, the Damore argument. Besides the fact that his psuedo science has been summarily handled[0], to consider his argument you then have to equally consider the possibility of sexism in academia pressuring women to not study these subjects and societal pressure their whole lives pressuring them to not persue these career paths.

There's also the idea that lack of women scientist "heroes" can be limiting (lack of role models). Basically the idea that if you stack the cards against a population, you're gonna see population-wide effects.

Given these data points, a biased hiring AI contributes to the problem. Therefore, it should be fixed, along with the above points.

[0][https://www.bbc.co.uk/news/world-40865261](https://www.bbc.co.uk/news/world-40865261)

[0][https://www.theguardian.com/technology/2017/aug/13/james- dam...](https://www.theguardian.com/technology/2017/aug/13/james-damore- google-memo-youtube-white-men-radicalization)

<comment><USER49> > _There 's also the idea that lack of women scientist "heroes" can be > limiting (lack of role models)_

This one is a bit weird, computer guys were always "nerds" and "geeks" to stay away from.

<comment> crooked-v ...starting in the 80s, which is also when the percentage of women going into computer fields started dropping like a rock.

</comment></comment></comment></comment></comment><comment><USER50> Given that Amazon is so far unable to successfully recommend any product which I actually want, even given the vast dataset of my Amazon purchase history, I am not remotely surprised that their engineers can't successfully develop a people recommendation engine either.

<comment><USER51> \- If you hired this male candidate you may be interested in this identical male candidate.

</comment></comment><comment> village-idiot We're going to keep seeing stuff like this until people finally realize that AI isn't some magic tool that solves every problem. It still reflects the biases and assumptions of its creators and its training data set.

</comment><comment><USER52> I'm doing a bunch of ML on a very different data set -- looking at what people eat (survey data). What's interesting to me is that if you do principal component analysis, for instance, there are some differences between the boys & girls in the sample, but they're not very distinct. If you do clustering or random forests on the dietary intakes of the whole cohort, you get mushy and unclear signals. If you split the survey respondents and bin by age and gender, and run different models for each, suddenly signals jump out of clustering _incredibly_ clearly! What's weirdest is that you get some of the _same_ dietary clusters for the different demographic groups -- but those clusters were not evident when you did clustering across the cohort.

It's surprising to me that Amazon didn't (apparently) try different models for different populations. Sure, it might open you up to criticism, but there are some good data-driven reasons to do so. Women's colleges won't show up with regularity on men's resumes, for instance. Similarly, there are fraternities and sororities around engineering and STEM that may provide different signals, but won't appear equally distributed on men's & women's resumes. Language use on resumes does differ by gender, and using "Captured value of $100 million by..." rather than "Created value of $100 million by..." may describe the same project. (I gotta say, using verbs at all seems silly, since it really is about how well you market, rather than what you did.)

So, curious about the model. Different models for different subsets of the training data can lead to big wins.

<comment><USER53> Off topic, but I'm looking at similar data. Are you talking about a public source (eg NHANES) or something else?

</comment></comment><comment><USER54> Once I worked for a startup selling fresh human baby milk to mothers who couldn't produce milk. I was contracted to write a supply side AI that would find people willing to sell the milk they produced to us. The AI had the exact opposite bias as the one in this article... it showed extreme bias against men.

I also wrote a similar AI for finding surrogate pregnancy candidates and it also showed bias against men.

Goes to show how AI can fail and be incredibly sexist.

</comment><comment><USER55> I hate the clickbait way in which this story has spread across the net. "secret AI recruiting tool" sounds like Amazon did something nefarious. Instead they built a tool, found out it was broken, and didn't deploy it.

The actual newsworthy part, which is getting slightly stale, is that it was influenced by the data bias.

I am not even a fan of Amazon but I think this is unfair to them. They did the right thing here.

</comment><comment><USER56> I feel like many comments here are taking this story on face value, but the cynic in me reads this as a planned leak to skapegoat a (hitherto unknown) AI system for their existing hiring biases. Public perception of AI is more aware of model biases nowadays, and we seem all to willing to accept this explanation over the simpler explanation that tech hiring at Amazon is broken in the same way it is everywhere.

</comment><comment><USER10> There's something I don't understand in stories like this. It ought to be relatively straightforward to correct biases like this. All you need to do is train a model to explicitly classify gender from resume's, and then use _that_ model to de-gender resume's before passing them on to the hiring model. Is there some reason people aren't doing this?

</comment><comment><USER57> I think there's also a subtle point here that an AI figured out that Amazon hiring was biased more quickly than Amazon itself.

I wouldn't go so far to say that running your history through an AI is necessarily proof of anything though (esp. in court). Imagine using another company's historical data to suggest that they're discriminatory.

</comment><comment><USER58> I'm honestly pretty shocked that Amazon went ahead with this which is such an obvious legal liability in an extremely touchy area of business practice. Everything about this should have been a red flag that meant it never should have gotten off the ground, let alone been in place for years.

<comment><USER59> You don't think they should try new things, even though they might not be successful?

<comment><USER58> When it systematically and illegally oppresses a class of people in an obviously ethically dubious manner, I would expect a certain level of caution.

</comment></comment></comment><comment><USER60> The thought that you can somehow suss out with any consistent performance who will be good or not at their job based solely on a training set consisting of resumes is so banal and inept that its laughable. Team building has so much more to do with assembling the right kinds of people with the right talent and personalities that no present day machine learning could ever hope to come close in effectiveness to a thoughtfully applied interviewing and hiring process.

It is clear whatever executives in charge of this project haven't the first clue of whatever it is they are doing. It lacks not only any technical deliberateness but also fails the common sense test. I really wouldn't want to be working for such people.

</comment><comment><USER61> I know diversity in tech is a hot button issue but I think hidden underneath the politics of it all the most important reason to champion more diversity is AI.

I don't think we're anywhere close to general AI so any AI system out there is built on what we feed it. If your music suggestion algorithm has a biased AI that's one thing, but when you're using AI to make critical decisions in society like who gets hired/recurited, medical diagnoses and other things you need to be extremely careful.

When designing AI models the broadest set of viewpoints should be considered. Any piece of AI is simply a reflection of it's creators, we need to make sure that some sort of equitable consensus is reached before deploying AI to critical human scale issues.

</comment><comment><USER62> It seems strange to me that they would train the system on resumes submitted to them over the past 10 years. I presume they judged the ones they hired as 'successes' and those they did not as 'failures'? Or did they judge all submissions as successes, expecting to be testing for 'does this person fit in with the group of people who submitted to us over the past 10 years?'

Neither matters much, neither makes sense. What they should have been doing was training it on the resumes submitted by employees who then went on to be very successful within the company. Those are the successes. Those are what you want more of. And, probably far less likely to be weirdly biased by gender or race or whatnot.

</comment><comment><USER63> What would happen if you removed gender from all HR and recruiting systems and then retrain the AI? Or for that matter, remove ethnicity, age, creed, etc... Is there any reason we need to be more specific?

Race: Human

Gender: Yes

Age of legal contractual consent: Yes

<comment><USER64> The AI did not have access to gender. It was just word weighting and it turned out that words that could be linked to females ended up with applicants that had a negative outcome. Like the article says the AI ended up giving a negative weight to any resume containing the word _women 's_ as in _women 's [---] club_, or those that mentioned certain all women's colleges.

<comment><USER63> It should be fairly easy to filter / replace all of that. The same logic can apply. Can we add some simple filters and re-train it?

<comment><USER41> >Amazon edited the programs to make them neutral to these particular terms. But that was no guarantee that the machines would not devise other ways of sorting candidates that could prove discriminatory, the people said.

If you've ever trained a NN, you'll know that they are exceedingly clever in finding patterns that fit what you're training for. You can remove the word "women's" and other obvious things from being considered, but I promise you, if there's another non-obvious patterns that are more likely to apply to the women candidates, the AI will find them and use them.

</comment></comment></comment></comment><comment><USER65> Probably being used for age bias as well...

</comment><comment><USER66> I'm curious: how is it that this submission has not "reused" the submission I made eight hours before? In my experience, sending a link that is already there simply upvotes the existing submission.

<comment><USER67> After a certain amount of time, the same link can be posted by a different user. I'm not sure if there is a point or point vs time qualification on that. You can post old, popular stories again for example.

Your submission was made 11 hours ago. If they merely applied an upvote to that existing story after 10 hours, the point time value rot would be such that it would have greatly reduced impact at ranking the story to the front page (ie it would be nearly useless for discovery purposes).

</comment></comment><comment><USER68> > "Gender bias was not the only issue. Problems with the data that underpinned > the models' judgments meant that unqualified candidates were often > recommended for all manner of jobs, the people said. With the technology > returning results almost at random, Amazon shut down the project, they > said."

Doesn't this mean the headline is incorrect, incomplete and/or (intentionally) misleading?

</comment><comment><USER69> "Instead, the technology favored candidates who described themselves using verbs more commonly found on male engineers' resumes, such as "executed" and "captured"

This is the most interesting part to me. It's suggesting that men and women think about things differently. Which, if true, suggests a lot of other possibilities that have relevance when hiring for certain positions.

</comment><comment><USER70> One of the first good critiques of the "oncoming AI apocalypse" that I've seen is exactly this: the encoding of existing biases into the resulting AI system.

But, it also means that bias is now measurable...

...and there were more than a few papers at NIPS that were directly dealing with "fairness" in a NN, aimed at addressing and using these issues and effects.

<comment><USER71> _Weapons of Math Destruction_ offers good discussion on this problem, if you want to read more about it.

</comment></comment><comment><USER72> I'm pretty sure this tool reached out to me at one point. Here is the email excerpt:

[http://pad.yohdah.com/638/amazon-ai-recruiting- email](http://pad.yohdah.com/638/amazon-ai-recruiting-email)

</comment><comment><USER65> Wouldn't an easy way to eliminate bias be to remove any algorithms that use name recognition and gender? If the Ai doesn't have this data to "reason" from wouldn't it level the playing field?

<comment><USER73> If, and only if, those are the only differences between candidate resumes. I don't think that's a reasonable assumption to make. Work history differences, sentence structure, word choices - all of these can quietly reflect gender differences.

</comment></comment><comment><USER74> Every product in development is going to be secret, but Reuters tries to make it sounds as if some sort of conspiracy was going on. What's next? "Jeff Bezos secretly goes to the restroom"?

</comment><comment><USER75> this sounds like a bad ML tool to begin with because the input data sucks. it's probay judging who can write a "good" resume. plenty of bad candidates write good resumes. and good hires write bad ones

</comment><comment><USER76> How do they measure non-bias? i.e when is it working as intended?

My only worry is that they are aiming for 50/50 which doesn't reflect the underlying gender ratio of the developer pool.

</comment><comment><USER77> The discussion here (versus other channels) is so much better. Not that I expected any different. I'm just glad no one is using this media report to confirm their own biases.

</comment><comment><USER78> What if we trained a bunch of robots with AIs embedded and they turned out to be racist misogynists? There has got to be a science fiction story about that...

<comment><USER79> You mean like Tay?

</comment></comment><comment><USER80> Have they not removed protected characteristics from the data? If they had, AI wouldn't know about gender and there would be no bias.

</comment><comment><USER81> It is no secret that more men pursue careers in this field. How can you expect any algorithm to produce an equal number of men and woman applicants? If it did this then that would be actually biased in favor of woman.

If this is what they want then they have to feed their neutral algorithm pre- biased data to get their expected results.

<comment><USER69> I think it isn't equality in numbers but in activity screening out women.

So there may still be a 8:1 ratio of men:women but according to the article, it would seem than even that 1 women would have been negatively impacted.

I am not arguing the validity of their conclusions, just saying that I don't think that it's about equal numbers: given a male and female applicant, according to the article, women would have had an unequal chance of passing the screening. (Now if that inequality was for valid reasons, that to me, is an open question, but the article indicates that there was unjustifiable bias.)

<comment><USER81> Fair enough. I'm still skeptical of their interpretation without more details, but anything is possible.

</comment></comment></comment><comment><USER82> If the gender was passed as a "feature" in the machine learning model, then the company may have been biased. If not, then the majority of the women's CVs were inferior. This important information is not present in the article.

<comment> core-questions This entire article is a strong hint that the AI system simply picked the better resumes, where better = more likely to get hired, exactly as it was designed to do. The fact that more men than women satisfy these requirements should come as a surprise to precisely nobody.

Fact is, there are more men than women working in tech, especially seriously hard-core stuff like these big companies need. This is most likely out of their own personal volition, and no matter how much outreach we do, this is likely to stay the case for generations.

Of course, that's also an egregiously wrongthink position to take. Double plus ungood.

</comment></comment><comment><USER83> This never has been a problem about gender issues. It's about finding bugs in magical machine learning algorithms. Im sure the algorithm suffers from many other deficiencies but gender bias is the one people write articles about.

</comment><comment><USER84> So why is it not ok that women are penalized by being statistically less likely to get an offer yet it's just fine to penalize men on auto insurance for being statistically more likely to cause an accident?

<comment><USER51> Because men are statistically more likely to cause auto accidents. There is no evidence that women are not statistically more likely to be bad at getting good work done at companies like Amazon. It's pretty simple.

<comment><USER54> Do you have a source for your second statement?

</comment></comment></comment><comment><USER85> Stupid question probably but why not have two models.

<comment> T-hawk Because evaluating differently based on gender _actually is discriminatory_.

<comment><USER86> Well, that sounds about right, then - every proposed solution to subtle, unconscious bias inevitably turns out to be explicit bias in the other direction.

</comment></comment></comment><comment><USER87> The article never mentions what form of supervision Amazon used for building this model.

I can easily see a bias towards a particular gender arising, even when a team had intentions to do, simply because of how the data is selected.

________

Case 1 : They used similarity statistics between candidates that were hired and applicants.

This is the easy one. No need to label datasets. The approach is semi- supervised. It will also 100% cause a bias towards employees with similar profiles as those already working at Amazon. (ie. men)

________

Case 2 : They manually labelled/ranked a dataset of resumes and assigned them scores. (more likely)

Here the implicit bias of the mechanical turks / rubric would be visible. If higher scores were assigned to traditionally masculine activities, then male resumes would stand out. I doubt this was the case though, as people at Amazon are generally competent enough to not make such a trivial mistake. Also, gender only gets mentioned in non-technical skills (mean's team, women's club, etc), which in general are not the most relevant part of the profile anyways.

________

Speculation:

1.

> Instead, the technology favored candidates who described themselves using > verbs more commonly found on male engineers' resumes, such as "executed" and > "captured," one person said.

I wonder if this has anything to do with gender at all. All good resumes that I've read use action words irrespective of gender. Maybe type A personalities use words like "captured" more often than type B ones, and the % of men in type A categories are greater. Would discrimination against women in such a case be unfair ?....Maybe.....Maybe not.

2.

Extracurricular activities are more prominent on weaker resumes than stronger ones. So, the model may be weighing down resumes with too much extracurricular fluff vs technical skills. Men's activities are rarely prefaced with the word "men" in it. (they would just say Football team, Chess team). Women's activities on the other hand, always have the word "women" attached to it. If the extracurricular activities were penalized, then the words inside of them, including "women" would also be penalized. Thus, the model learns a latent gender bias without any bad intentions.

_________

In ML, one of my favorite statements is : "The model is only as good as the data it is trained on." If the data is not sufficient, rich enough or prepared in the correct manner, then unintended consequences are nearly guaranteed.

</comment><comment><USER88> Things are going to get very interesting when AI inevitably reveals consistent differences in performance among genders.

There's only so much you can sweep under a rug.

</comment><comment><USER89> Is it a Microsoft Tay problem?

</comment><comment><USER90> Gender. I am more concerned about race and ethnic background. Why is this never addressed?</comment>


Facebook's data gathering hit by German anti-trust clampdown - <AUTHOR> https://www.reuters.com/article/us-facebook-germany/facebooks-data-gathering-hit-by-german-anti-trust-clampdown-idUSKCN1PW0SW <comment><USER2> I wish anti-trust regulators would actually _do_ something. The unchecked power and behavior of corporations is depressing. Take WhatsApp away from Facebook. They broke the one condition there was for the acquisition to be allowed. They have no legal or moral right to continue owning it.

</comment><comment><USER3> If I remember correctly Facebook was only allowed to buy Whatsapp in France because they promised not to share user data between the two services. The German data protection agency basically didn't say anything only because the french said it already.

Now Facebook does it's mix-and-mash anyways.

I'd have expected to hear something out of France about this. Did I just miss it?

<comment><USER4> Facebook has already been fined by the European commission in 2017 to the tune of 110M Euros over this. It is a drop in the bucket compared to the billions Facebook paid for WhatsApp though - so small one suspect Facebook might have already factored it into the cost of the acquisition.

[1]: [https://www.theguardian.com/business/2017/may/18/facebook- fi...](https://www.theguardian.com/business/2017/may/18/facebook-fined-eu- whatsapp-european-commission)

<comment><USER5> Isn't the point of fines to punish past bad behaviour? Why aren't there escalating fines if a company continues to not comply with rulings? I mean it is not a one time thing that can't be changed.

Sorry for asking, I know next to nothing about how these laws work.

</comment></comment></comment><comment><USER6> > If Facebook fails to comply, the cartel office could impose fines of up to > 10 percent of the company's annual global revenues, which grew by 37 percent > to $55.8 billion last year.

I hope they go for the maximum fine.

</comment><comment><USER7> What about Google? (With their Adsense, analytics, email, search ... setup ...)

<comment><USER8> I hope Google will also be one day forced to use permissions on per app and per service base.

If I want to give Google Maps access to my location it means only Maps and for limited scope not all across Google ecosystem.

Android is constantly nagging me to turn on location and wifi scanning for example to use Google Home. It doesn't make sense - they could use SSID of my home wifi to make sure that I am home. Where my home is is not their business.

<comment><USER9> So you want to use ad-supported services without feeding the ad-system that keeps the services running? Why do think you have a right to use Google Maps without supporting the underlaying business model?

<comment><USER10> Google maps can charge for API use. They already do. Services can charge money -- not everything has to be ad supported. Business models aren't inviolate things -- they exploit existing regulations. When the regulations change, then so do the business models.

<comment><USER11> Then why do you use ad-supported services rather than going for the ones which do not collect your data and instead charge you money directly?

<comment><USER10> Pretty much all services make money from tracking you because it's legal and doesn't cost them anything.

Even those that charge. E.g. google maps will charge the vendor and still track the user. A WSJ subscription will still have tracking code and third party js, etc.

As long as it's free for the service and there are no penalties, then of course they will try to collect as much data on you as you allow them to collect. But it's your responsibility as an internet user to prevent them from collecting anything that you don't approve of, and to block third parties from injecting their code into your equipment unless you vet that code or otherwise trust it. When stuff like spying and data collection is outlawed, this creates economic room for honest players to participate in honest business practices. We're just not there yet.

</comment></comment></comment></comment></comment></comment><comment><USER12> Facebook's statement is... interesting.

> The Bundeskartellamt underestimates the fierce competition we face in > Germany

Which competition? The only viable social network in Germany that came close to Facebook was Lokalisten, which shuttered finally in 2016.

<comment><USER13> Domestic data hoarders and advertisers - extremely invasive and territorial. The difference is that none of them runs social networking website or mobile messenger app, thus without consumer facing facade many uninformed assume they don't exist. e.g. their media and retail oligopolies like to roll out their own ad, tracking, and loyalty programs to have full control of the data. They don't let anyone in and are trying to expand, clumsily, to other EU countries.

<comment><USER14> What does "other data collectors exist" have to do with Facebook's status as the dominating social network (95% (measuring DAUs)/80% (measuring MAUs) market share according to the press release)? Facebook also clearly cites other social media companies/networks from a wider scope in defense, not data collectors.

<comment><USER15> there are other relevant collectors as well for example Deutsche Post (German Postal Services), see [https://www.dw.com/en/deutsche-post-sold-voter- microtargetin...](https://www.dw.com/en/deutsche-post-sold-voter- microtargeting-data-to-cdu-and-fdp/a-43218488)

</comment></comment></comment></comment><comment><USER16> > "The decision by the cartel authority, known as the Bundeskartellamt in > German, allows Facebook to continue collecting data from its services like > WhatsApp and Instagram. But the company will not be permitted to link this > information to Facebook user accounts unless a user has explicitly > consented."

Facebook will just get away with a dark pattern of showing some new consent agreement to "improve your user experience" and nag people until they accept out of frustration.

Can't any regulators just say, "No, you can't do this under any circumstances. Period."? Or come up with laws that prohibit these practices, knowing fully well the size and influence of Facebook and its properties (applying it to similar companies/models as well)?

</comment><comment><USER17> Part of me cheers.

The realist in me goes "yay, more annoying consent popups" :(

</comment><comment><USER18> I hope they force FB to destroy all information harvested on users in the past too.

</comment><comment><USER19> Finally, it is time for gesichtbuch.de to shine

<comment><USER20> It's owned by facebook.

</comment></comment><comment><USER21> What really interests me is the lack of scrutiny towards Google. It all seems focused on Facebook, meanwhile what Facebook did was set a trend that others followed. Hopefully there is more to come, but it is definitely too little too late.

<comment><USER9> Whataboutism?

<comment><USER21> I did not mean for it to come out seeming like whataboutism. I just meant we should also look into Google's practices. Absolutely not excusing Facebook for what they chose to do.

</comment></comment></comment><comment><USER22> Going by Facebook's history .... what are the chances they abide by any of this?

Facebook has shown they're going to do what they want how they want regardless of public perception, deals with other companies, other company rules, and arguably laws already in place.</comment>


Court appoints 5 LotR experts to decide if comparison to Gollum is an insult - <AUTHOR> https://www.washingtonpost.com/news/morning-mix/wp/2015/12/02/a-turkish-court-appointed-five-lord-of-the-rings-experts-to-figure-out-whether-this-gollum-meme-is-offensive/?tid=hybrid_collaborative_1_na <comment><USER2> Wow. That has a lot of potential interpretations.</comment>


A Healthy Dose of Fear is Appropriate When Dealing with the Press - <AUTHOR> http://randfishkin.com/blog/164/healthy-dose-fear-appropriate-dealing-with-press

<comment><USER2> I can totally relate to the part about an article coming out about you where they didn't even bother to contact you.

That used to happen at reddit all the time! I'd see an article about reddit and think, "You know, all you had to do was read our blog if you wanted this to be even remotely accurate!".

<comment><USER3> Got two emails and comments to the same effect. Not sure why this is such a common problem among sources that are recognized/respected in the tech world. I'd think once a reporter makes this mistake twice, their career is finished, but that doesn't seem to be the case at all.

<comment><USER4> This goes well beyond tech reporting. I've had very respected news organizations attempt to do much the same shenanigans on issues with rather more import than our inside baseball usually has. (Attempting to spoonfeed me a desired narrative directly contradictory to actual facts and use me for unbiased, expert pull quotes supportive of the reporter's narrative.)

</comment></comment></comment><comment><USER5> Can't agree more. Having once been a publicist, the first rule of dealing with the press is to realize that they have ALL the power and that anything you do must be in service of that.

The notion that everything is on the record is of particular note. You, run into a journalist at a party and mention drunkenly your product launch strategy. Consider it out of the bag because it's fair game to report now. Expect a call the next morning.

Moreover that extends to anyone with familiarity with the situation. Your dirty laundry can be aired in just such a way. Your employees offhanded comments are fair game for stories.

I will note that the one thing you can and should do is contact a reporter's editor if they've run a story about your company without calling for comment. Be totally sober about it and make sure you've run whatever you plan to say by someone with emotional distance from the situation. It's easy for it to come off as "WAH WAH WAH why didn't I get called to talk about this bad thing" and the best way for you to get your side across is to be as detached as possible.

</comment><comment><USER6> From what I was told

1 Record all conversations with them.

2 Only talk to journalists from Proper organisations.

3 Only talk to journalits with domain Knowledge.

4 Only talk to journalists you and ideally other People you know trust.

5 Know what "Off the record" means and ask the hack which this interview is.

6 Also if you're an employee know where you stand legally - get Authorisation - Still wish BT had let me do the post to alt.2600 that BT's press office asked me to do.</comment>


Introducing Keras 2.0 - <AUTHOR> https://blog.keras.io/introducing-keras-2.html?t=1 <comment><USER2> Keras is so good that it is effectively _cheating_ in machine learning, where even Tensorflow tutorials can be replaced with _a single line of code_. (which is important for iteration; Keras layers are effectively Lego blocks). A simple read of the Keras examples ([https://github.com/fchollet/keras/tree/master/examples](https://github.com/fchollet/keras/tree/master/examples)) and documentation ([https://keras.io/getting-started/functional-api- guide/](https://keras.io/getting-started/functional-api-guide/)) will let you reverse-engineer most the _revolutionary_ Deep Learning clickbait thought pieces.

It's good to see that backward compatability is a priority in 2.0, since it sounds like a lot had changed.

</comment><comment><USER3> Note that there are breaking changes, and it seems the docs online already point to the new, 2.0 version.

So if you are relying on the docs to edit old code, you may become a teensy bit frustrated!</comment>


The developer board instead of job boards? - <AUTHOR> https://devmountjob.com/what-its-all-about <comment><USER2> >To sum up, let's just say there are two groups: those who are not interested in changing their job, and those who, for whatever reason, are.

There's existing terminology for this. Passive and active candidates.

The whole concept sounds promising. It also sounds very similar to the now defunct hiremyfriend.io -

[https://web.archive.org/web/20150313052317/https://hiremyfri...](https://web.archive.org/web/20150313052317/https://hiremyfriend.io/)

[https://techcrunch.com/2013/07/17/new-london-startup-lab- mak...](https://techcrunch.com/2013/07/17/new-london-startup-lab-makeshift- gives-birth-to-its-first-mvp-hiremyfriend/)

[https://news.ycombinator.com/item?id=6057050](https://news.ycombinator.com/item?id=6057050)

<comment><AUTHOR> I have never heard about hiremyfriend before. The idea is quite similar but I would like to avoid any networking there. I will try to find more info about hiremyfriend and hopefully some post mortem about their failure :). Thank you!</comment></comment>


Mega-victory: Kim Dotcom search warrants "invalid," mansion raid "illegal" - <AUTHOR> http://arstechnica.com/tech-policy/2012/06/mega-victory-kim-dotcom-search-warrants-invalid-mansion-raid-illegal/ <comment><USER2> I hope Dotcom comes out of this victorious and relaunches MegaUpload. It was the best free one-click hosting site out there - everything available at the moment sucks. Not to mention it would be a wonderful "fuck you" to the US government, abusing its global hegemony for personal purposes.

<comment><USER3> You do realize that if people like Kim Dotcom ... is that really his name? Fuck him, apparently his name is Kim Schmitz and I don't care what stupid name he changed his name to.

Wait, sorry, I got distracted, let me start my paragraph again.

You do realize that if people like Kim Schmitz win their legal cases it'll just mean a massive push to change the law? If current laws aren't good enough to stamp out things like MegaUpload (which, let's face it, is a massive scale piracy site which exists to make money off other people's IP) then the laws _will_ be changed until they can.

<comment><USER4> I'm going to throw in my opinion on the name thing. I welcome disagreement, especially in this specific case, but there have been 3 or 4 different occasions lately where I have seen something similar come up.

Kim Dotcom legally changed his name, for whatever purported reasons (publicity, most likely). He willfully has chosen this as his legal name, and while the name (or his reasons) might be silly or frivolous, I think he is owed the due respect of calling him by his chosen name.

Other noteworthy examples of late have been Ron Artest changing his name to "Metta World Peace" and Chad Johnson to "Chad Ochocinco" (which he may have changed back, I cannot recall at this time).

Their chosen names may too be silly or frivolous, and I have seen media examples where they have called it out and said "forget that, I'm calling him Ron Artest/Chad Johnson, I don't care what his legal name is."

In my belief this is patently disrespectful, in the same way that it was disrespectful for people to call Muhammed Ali "Cassius" after he changed his name, and in the same way it would be disrespectful for me to call you by some name that you did not wish to be called by (legally, for religious purposes, or any other personal reason).

I do fully understand your stance and I wouldn't call it "wrong", I just don't agree with it.

<comment><USER3> >In my belief this is patently disrespectful, in the same way that it was disrespectful for people to call Muhammed Ali "Cassius" after he changed his name

Possibly, but since I don't respect Kimmy, it's appropriate that I should call him by his (sensible) birth name rather than the (ridiculous and embarrassing) name he chose for himself.

I don't respect Cassius Clay much either. Seriously, all that bragging is _gauche_. Saying you're "the greatest" just because you're pretty good at punching people? Get back to me when you've won a Fields Medal, dude.

<comment><USER5> > Saying you're "the greatest" just because you're pretty good at punching > people? Get back to me when you've won a Fields Medal, dude.

If there were such a thing as a Fields Medal for boxing, Ali would have won it. He wasn't just "good at punching people", he brought in techniques that changed the sport.

It's fine that you are comfortable being as ignorant about boxing as most people are about math. That's your prerogative. That said, dismissing someone's accomplishments based on your ignorance is a shitty thing to do.

<comment><USER3> No, what I'm saying is that punching people isn't a worthwhile endeavour.

<comment><USER6> You are not the grand arbiter of worthwhile endeavours.

</comment></comment></comment></comment></comment></comment></comment><comment><USER7> Interesting comment stream.

In one view you have 'America, World Police' being handed a defeat. That is a good thing because I believe that both the precedents the USA is trying to set on the world scene are wrong, they will also come back to roost in unpleasant ways.

In one view you have a victory for the Libertarian Internet which gives us a stronger 'nation independent' view of the Internet, but this is part of a wider set of events which are trying to figure out the role of the 'information-state' if such a thing could exist. This is stuff SF writers have chewed on considerably and to see it in 'real life' is fascinating.

In the third view we have someone who has many of the characteristics of 'nerds' which seem to have taken on some of the aspects of a counter culture.

The interesting things is that this fiasco has neatly tied together three on- going 'conversations' [1] about the autonomy of the Internet, the role of nation states in world government, and the rise of 'nerd culture'.

As we enter the 'post-American' century the discussion of world government will be be, by far, the most influential on our day to day lives, but the autonomy of the Internet will also become a central theme, much as human rights is a central theme in our interactions. The story of Kim Dotcom will then fade to being the tag line for one of the things that shaped how we feel about the first two.

[1] The term 'conversations' here is used to indicate that its a topic of discussion at all levels of our society with various competing views fighting for dominance as the 'group consensus'. This is also the name of the process by which a community arrives at a common understanding much like two people conversing about the topic might come to a consensus.

</comment><comment><USER8> I'm curious now. Does the warrant being invalid open the US up to a class action suite from people who lost data? I know nothing about the law regarding this sort of thing but its what immediately leaped to my mind when I saw this article.

[Edit: spelling]

</comment><comment><USER9> Does Dotcom have any legal recourse to sue the FBI/US Gov't if he is found not guilty/the case is thrown out?

People need to realize that this kind of action is not O.K., and those responsible should be brought to justice.

<comment><USER10> Nobody can sue the US Government without the US Government's permission.

<comment><USER11> US citizens most certainly can.

<comment><USER12> No, they cannot. Sovereign immunity is absolute. US citizens can only sue when granted permission.

As 30thElement noted, the Federal Tort Claims Act grants this permission to sue. But there is no such right, and it is within the Federal government's power to change its mind and repeal the statute.

<comment><USER11> You mean civil lawsuits? That's not what I was talking about.

<comment><USER10> What were you talking about then? "Suing" is always a civil action. Taking someone to court for a criminal act is called prosecution and can't be done by just anybody.

</comment></comment></comment></comment></comment></comment><comment><USER13> Previous discussion: <http://news.ycombinator.com/item?id=4170398>

</comment><comment><USER14> I asked this in the other thread but no one responded so I'll try here again: This appears related to Dotcoms house raid and seizure. Is the raid on all of the servers in VA still valid? I'd assume that is where the majority of all of the evidence in this case lies, and if that is still valid there is still a huge case to be had. Does anyone know?

<comment><USER15> The raid in VA is "valid" until a US court says otherwise. The NZ court only has jurisdiction over those parts of the case that pertain to NZ, although it _has_ ordered the US to turn over any evidence they have on K.C to his defense. Again, though, that's only as it relates to the extradition process.

</comment></comment><comment><USER16> Cold comfort, since the FBI will keep his servers tied up in knots until the end of time and most likely, refuse to pay for the loss of business.

</comment><comment><USER17> Wonderful! Kim Dotcom doesn't seem like the cleanest of individuals but the US government is even worse. I hope that people in NZ law enforcement suffer enough career damage that other countries think twice before simply bending over for US exceptionalism.

</comment><comment><USER18> If the 150 TB's slowed down local law enforcement enough to make them mishandle the warrant, any bets on how many petabytes he'll have waiting for them next time? : )

</comment><comment><USER19> The US government has successfully destroyed the world's largest storage medium that they couldn't just walk in and spy on without detection (US-based companies are subject to The Patriot Act and other "laws" that stand in defiance of The US Constitution).

I don't see how this counts as a "victory." It is probably all part of the game plan.

<comment><USER17> But on the good side, it should be harder to get other countries to simply play the bitch and do whatever the US says in future.

<comment><USER20> That's not how these things work. Governments don't give up. Next time they will not make same mistake or get laws passed so this is no longer mistake/illegal or just do it again the same and gamble they get a more cooperative judge.

<comment><USER17> Every country is not the US. There are still a lot of countries where the people have some say in what goes on and fight against such laws getting created in the first place.

<comment><USER21> Care to name them? I've been looking for them, and can't find any.

<comment><USER17> Well I can think of at least two direct democracies: Switzerland and Iceland.

<comment><USER21> Switzerland is better than the US when it comes to corruption, but it's a far cry from people really having a say.

Iceland HAS recently changed from a politician's country to the people's country, but it took going broke to do so. (If it wasn't for people literally in the streets with pitchforks, the Icelandic prime minister would have signed the country away to European interests).

But I'll accept the premise. So, we've got 2 countries out of a couple of hundreds. That's not "a lot" by any meaning of the word. That's one country (Switzerland) that has recently demonstrated it will do the US' bidding about bank secrecy, despite hundreds of years of tradition (with no asking the people if they like it or not), and Iceland - a country of <400,000 people. That's slightly larger than Rochester, NY. It's comparable to the number of people who lose their job in the US every week.

</comment></comment></comment></comment></comment></comment></comment><comment><USER22> Kim will get his servers and hardware back.... Pulverized to pieces by the might of a legion of pissed off FBI data workers who had their power checked and found lacking. You gotta set an example that even when you win a battle against organized crime, they make you pay dearly for every inch.

<comment><USER23> I thought he had leased them all.

</comment></comment><comment><USER24> His wife is hot.

<comment><USER25> She's only there for his money.

Kim isn't a hero, nice or generous person. He's just a geek with money who is now 'abusing' his new found wealth.

I don't look up to him, personally.

<comment><USER26> Well you are one of the few on HN. Although a lot of it is probably more of a hate America thing, which is always popular.

<comment><USER27> Right, because disagreeing with U.S. government policy is equivalent to hating the entirety of America.

<comment><USER26> I meant a sympathetic view towards dingleberry was partly due to a dislike of America( government, corporations, certain people) and not because they think he is a swell chap.</comment></comment></comment></comment></comment>


Silicon die analysis: inside an op amp with interesting "butterfly" transistors - <AUTHOR> http://www.righto.com/2018/06/silicon-die-analysis-op-amp-with.html <comment><USER2> Ken's reverse engineering articles are always a real pleasure to read, but this one is special in that it looks at an analog chip and how typical analog building blocks (current mirrors, differential pairs) are implemented in silicon.

Also, if you've every looked at an analog IC's schematics and wondered what those weird "double collectors" BJT are ([https://electronics.stackexchange.com/questions/105777/what-...](https://electronics.stackexchange.com/questions/105777/what- do-multiple-emitters-collectors-imply-in-a-bjt-schematics-symbol)) , there's a pretty decent description in there of what a BJT actually looks like when implemented in silicon.

</comment><comment><USER3> Funny, I have actually used this one, the 4 means 4 OPAMPs in a single chip. IIRC there's also the TL082 with 2 OPAMPs.

Much more performant than the 741 (might have to do with 2 things: the 741 came early and was one of the pioneers and it is BJT only)

It's also curious how the the big butterfly transistors are at the input, components with a big die size are usually big for a reason (usually power).

One extra fact, the compensation capacitors are more like a wrench on gears and actually make the circuit "worse" (it lowers the overall bandwidth). But it is needed for stability purposes because good amplifiers will have a tendency to oscillate by themselves.

<comment><USER4> Is there a general purpose op amp that is the modern equivalent of the 741? Something with a much more recent design with widespread use.

<comment><USER5> What's wrong with the 741?

The 071 is also "jellybean" enough; both are still currently used in modern designs because how dirt cheap they are.

But they are "high voltage", non rail-to-rail anything, so people tend to use more modern options for new designs.

<comment><USER6> 741 problems: terrible bias current, offset, input range, output range, power consumption, voltage requirements, GBW, noise.

They're just horrid. Fine for noddy stuff like power supplies and PID loops etc though.

</comment></comment></comment></comment><comment><USER7> While you marvel at this circuit remember that it was invented decades ago, for many purposes it is still state-of-the-art, and it costs fifteen cents.

<comment><USER8> I sometimes wonder whether integrated-circuit manufacturing wasn't sent to us by time-travellers from the future. Our ability to manufacture useful things, on such tiny scales, to such high precision, doesn't seem to match up with our comparatively-poor capabilities in other areas of manufacturing.

<comment><USER9> There's a series on transistors that covers this. There's basically no mechanical process since everything is done via etching and litho. Leads to the results you see.

<comment><USER10> Please link it.

<comment><USER11> Here's the start of the series: [https://technicshistory.wordpress.com/2018/01/20/the- transis...](https://technicshistory.wordpress.com/2018/01/20/the-transistor- part-1-groping-in-the-dark/)

</comment></comment></comment></comment></comment><comment><USER12> I have a few questions about the JFET diagram and JFETs in general if someone wouldn't mind answering:

In the graphic we see S, G and D - Source Gate and Drain. Is the source permanently connected to power? If so do all transistor in a circuit connect to a shared power rail?

Must voltage always be present on both the source and gate in order for current to flow to the drain?

Lastly when the transistor is switched "on" does voltage leaving the drain then become input for a gate of some neighboring transistor in the circuit?

<comment><USER13> > Is the source permanently connected to power? If so do all transistor in a > circuit connect to a shared power rail?

Looking at the TL084 schematic, the JFET source is fed from another transistor (a current source in a current mirror). Some of the transistors are connected to V++ and some are connected to V-- but many of them are not connected directly to any power rail.

> Must voltage always be present on both the source and gate in order for > current to flow to the drain?

There has to be a voltage differential between the source and the drain for current to flow. JFETs are "normally on" and get pinched off as the voltage differential between the gate and source increases.

> Lastly when the transistor is switched "on" does voltage leaving the drain > then become input for a gate of some neighboring transistor in the circuit?

Voltage doesn't really leave the drain, but the drain is connected to the base of another transistor, part of the second state of the op amp, where most of the amplification happens. In other words, the output of the differential pair is the input to the next amplifier stage.

<comment><USER12> Thank you for the detailed reply.

Would these last two points also apply to MOSFETs?

In the case of a MOSFET there obviously wouldn't be a "next amplifier stage" but would the output voltage be input to the next transistor's source or the next transistor's gate maybe?

<comment><USER5> For MOSFETs the story is about the same.

For the tree transistor horsemen (BJTs, MOSFETs, JFETs), they can all be fitted into similar transistor amplifier building blocks:

[https://en.wikipedia.org/wiki/Template:Transistor_amplifiers](https://en.wikipedia.org/wiki/Template:Transistor_amplifiers)

You can put these things one after the another to get more complex behavior.

Roughly speaking, common collector/common drain amplifiers have a gain of approximately 1, but has low output impedance, which makes it suitable for driving big loads (like the outside world). Common emitter/common source amplifiers have high output impedance but tons of gain.

The classic op-amp topology is a differential amplifier (long-tailed pair, in the wikipedia template) feeding into a common emitter/source gain stage, then into a common collector/drain follower output stage.

To answer your last question, the output of the gain stage is the drain of the transistor, that feeds the gate of the common drain output stage transistor.

The gate is usually the input terminal, but sometimes the gate is at a fixed voltage and the drain is the input terminal (cascode-y circuits are like this, aka common base or common gate).

<comment><USER12> Thanks I found the classification in that link quite helpful. Cheers.

</comment></comment></comment></comment></comment><comment><USER14> Does anyone have a good reference on how analog is done on logic processes, specifically the bits that have to interface off the chip, like gpio, lvds, and serdes. I'd much appreciate it.

There's something I once encountered, but can't find the reference to, that suggested a kind of "digital-analog" process whereby voltage levels were replaced by timing measurement (?) due to the limits of feature size in analog design. I'm not in the field so forgive my ignorance.

<comment><USER13> I don't know about modern chips, but 1970s and 1980s logic chips just used big MOSFETs to drive their outputs. There wasn't any weird timing magic going on.

There are also processes like analog BiCMOS that let you mix bipolar analog circuitry and CMOS on the same chip. I also wrote recently about the 76477 sound chip that combined I2L logic with bipolar analog circuits.

<comment><USER14> Thanks for pointing out your article about the 76477. Fascinating stuff.

As for my second paragraph about "digital-analog", this reference on VCO-based quantizers [1] is not the one I remember but I think is related. Not that I really understand the intricacies, but it's a way of doing analog at low- voltages and finer geometries.

[1] [http://ewh.ieee.org/r5/central_texas/cas_ssc/meetings/2012/1...](http://ewh.ieee.org/r5/central_texas/cas_ssc/meetings/2012/101112/vco_based_quant_perrott.pdf)

</comment></comment></comment><comment><USER15> Could a genetic algorithm have come up with this design?

(Electronics seems an interesting application area for automated design, since the search space is relatively small)

<comment><USER16> I modern chip design, simulated annealing is used for placement of parts to minimize the length of routes and optimize parasitic effects.

<comment><USER17> Not my area but I don't think that's the case for the analog ICs, or the analog part of mixed ICs.

The constraints for generic digital logic soup are comparatively "simple" : make wires as short and neat as possible and then check the (simulated) physical timing characteristics.

Analog is more artistic since any noise or crosstalk degrades the signal irreversibly (for low noise stuff) and any wire is a transmission line (for high speed stuff).

Actually even for digital I'm sure you still have to do manual layout for the most critical pieces of high-performance designs (say a register file on a nvidia gpu).

<comment><USER16> You can still run an analog design through simulation, and feed the results of simulation into a layout algorithm.</comment></comment></comment></comment>


Show HN: Gitteraqt – Native gitter client in QML/Qt - <AUTHOR> https://github.com/eklavya/Gitteraqt <comment><AUTHOR> I made a desktop app for gitter because using the browser client was growing more and more "inconvenient". I am a total noob so please help out. [https://github.com/eklavya/Gitteraqt/issues](https://github.com/eklavya/Gitteraqt/issues)

Mac and linux apps available to download here: [https://github.com/eklavya/Gitteraqt/releases](https://github.com/eklavya/Gitteraqt/releases)</comment>