[HN Gopher] Claims AI can boost workplace diversity are 'spuriou...
___________________________________________________________________
Claims AI can boost workplace diversity are 'spurious and
dangerous'
Author : laurex
Score : 146 points
Date : 2022-10-14 14:00 UTC (9 hours ago)
(HTM) web link (www.cam.ac.uk)
(TXT) w3m dump (www.cam.ac.uk)
| tomp wrote:
| _> "researchers from Cambridge's Centre for Gender Studies"_
|
| Are we sure we want to listen to scam artists?
|
| These _DIE industry_ professionals have been promoting and
| selling programs that "boost diversity" for a decade now and
| have approximately nothing to show for (aside from actual racism
| a.k.a. "affirmative action" that disadvantages Asians (in the US)
| / poor Eastern Europeans (in the UK) far more than whites/locals)
| sublimefire wrote:
| Was covered in Register as well
| https://www.theregister.com/2022/10/13/ai_recruitment_softwa...
| if you feel the need for a bit more sarcasm and some interesting
| comments :)
| not2b wrote:
| It's more likely to work the other way around. Suppose you train
| your system to try to match human hiring decisions, and some of
| your hiring managers have an unconscious bias against members of
| group X, enough to make a small difference in probability of
| hiring. Training the model will probably find that bias, and the
| effect may be that belonging to group X, alone, will have a
| negative weight in the model. So unconscious biases may become
| explicit.
|
| There are ways around this: test the model by presenting
| credentials that are identical except for (is in group X) vs (is
| in group Y) and looking for major discrepancies.
| [deleted]
| throwaway4993 wrote:
| This sort of thing needs more advertising and more noise. People
| really need to grasp that AI gets biased based on the training
| sets you provide. You can't just blindly trust the results.
| lm28469 wrote:
| I can't believe people are building such dystopic tech thinking
| they're doing something good
|
| > it cancels out human biases
|
| Just put us in a matrix style pod and let bots manage the world
| already, that's the only way you'll get rid of human biases
| testfoobar wrote:
| It seems to be heading down a purely non-sensical direction. I
| am waiting for us to be told that who we find attractive is
| biased. So our dating/mating pools should be restricted to
| correct this embedded bias.
| verisimilitudes wrote:
| White people who don't want to date black people and lesbians
| who don't want to have sex with transsexuals already suffer
| this.
| slavboj wrote:
| You don't need "AI" to deploy the cutting edge "hire the black
| fella" algorithm.
| jdmtheNth wrote:
| Just identify the AI as your obligatory diversity if questioned.
| dangerwill wrote:
| fallingknife wrote:
| 73% of Americans oppose affirmative action
| https://www.pewresearch.org/fact-tank/2019/02/25/most-americ...
|
| In 2020, CA voters (the most liberal state in the country)
| voted down a proposal to allow affirmative action by a 15 point
| margin.
| https://ballotpedia.org/California_Proposition_16,_Repeal_Pr...
|
| Opposing racial discrimination is moderate. You are, in fact,
| very much in the minority here.
| lm28469 wrote:
| Hire everyone you want, just don't use a shady """AI""" to tell
| you who to hire.
| [deleted]
| spoonjim wrote:
| Is anyone in power going to come out and say that it's very
| obvious that you don't need workplace diversity? Silicon Valley
| created trillions of dollars of wealth in just a few decades and
| it was staffed with 90% white, Indian, and East Asian men.
| whoooooo123 wrote:
| Ask James Damore how well that went for him.
| moralestapia wrote:
| Hire the most capable people for the job, period.
|
| I sympathize a bit with things like Affirmative Action, but at
| the end of the day, a person (disregarding gender, physical
| condition, race, etc...) who _earned_ it doesn 't deserve to be
| displaced by others who didn't.
| AlotOfReading wrote:
| That's not really the point. Any individual person will have
| their own biases and shortsightednesses, and basic assumptions
| about the world. People with similar backgrounds will tend to
| have similar biases.
| automatic6131 wrote:
| >Silicon Valley created trillions of dollars of wealth
|
| Lol. Lmao even. It's bizarre, because you could have said
| "trillions of dollars" and been completely correct.
| neon_electro wrote:
| Sure, you don't need workplace diversity to be insanely
| profitable. Do you think that's why folks are looking to add
| diversity to the workplace?
| spoonjim wrote:
| That's the argument that they make, duplicitously.
| AlexandrB wrote:
| A. Created trillions of dollars of wealth doing _what_? Because
| it seems like Silicon Valley is well on its way to being the
| new "big tobacco" with the level of surveillance and
| advertising that has been normalized.
|
| B. There's no way to know whether a more diverse Silicon Valley
| would have created $trillions + X dollars. Saying something was
| successful doesn't tell you anything about whether the
| alternative would have been _more_ successful or not.
|
| C. Wealth creation is a shitty metric. Ideally, all economic
| activity serves some purpose that makes actual people's lives
| better. This is why breaking windows to stimulate economic
| activity is considered a fallacy[1]. This is somewhat related
| to A, but saying "wealth was created so everything must be ok"
| is not very convincing.
|
| [1] https://en.wikipedia.org/wiki/Parable_of_the_broken_window
| inglor_cz wrote:
| The problem with B. is that there are infinitely many
| alternatives.
|
| Perhaps Silicon Valley would be even better off if all the
| developers had pet llamas at their disposal. But there is no
| llama industry that would lobby for that.
| hbrn wrote:
| > Wealth creation is a shitty metric. Ideally, all economic
| activity serves some purpose that makes actual people's lives
| better
|
| Shitty compared to what? Wealth does correlate with better
| life and is fairly easy to measure.
|
| Do you a specific "actual people's lives better" metric in
| mind?
| tenebrisalietum wrote:
| If you go from 0 to $10T dollars in the span of 5 years,
| but are now 25 and soon to die of cancer due to pollution
| caused by generating that $10T, your wealth as well as the
| effort to gain it was fairly useless.
| hbrn wrote:
| This proves that wealth as a metric is imperfect (like
| any other metric), especially if we are ignoring all
| other metrics (we're not). In no way it indicates that
| wealth is _useless_.
|
| Still waiting to hear which metric is more useful. It
| looks like you brought up life expectancy, but it does
| correlate with wealth. Unlike wealth, it's is extremely
| hard to measure due to enormous lag.
| spoonjim wrote:
| Sure, Silicon Valley could have been MORE successful. But
| it's a bit like telling LeBron James that he might have been
| better at basketball if he had eaten more protein in his
| breakfasts. It's like, really? I'm happy with being really
| fucking good. I don't need to change up anything.
| avereveard wrote:
| > white, Indian, and East Asian
|
| idk that sounds like diversity
| sbf501 wrote:
| This sounds like the start of a chapter in "Weapons of Math
| Destruction", but the "and then this bad unforeseen consequence
| occurred" half of the chapter is waiting to be written.
|
| I agree that AI "could" help remove bias, not to beat a dead
| horse, but it is subject to its own training bias. Maybe I'm
| stuck in 2018 NN models, and newer model authors and trainers are
| far more aware of that. But this seems like an extremely risky
| place to put AI.
|
| Not to be glib, but IMHO the biggest obstacle to diversity in the
| workplace is people who think "diversity in the workplace" is
| some kind of ideological conspiracy that is out to undermine them
| personally.
| Manuel_D wrote:
| My previous workplaces straight up reserved headcount for
| "diverse" candidates. In other words, we prohibited white and
| Asian men from a segment of our available headcount.
|
| There is no singular type of diversity initiative. Perhaps
| there are some that implement blind hiring to eliminate bias,
| or other methods of increasing diversity by eliminating bias.
| But I've never encountered one, in my experience there all just
| good old fashioned discriminatory hiring practices once you get
| past the effusive language.
| kypro wrote:
| > "diversity in the workplace" is some kind of ideological
| conspiracy that is out to undermine them personally.
|
| It doesn't have to be a conspiracy to be unfair. At my place
| the leadership team spend about 30 minutes every week
| complaining about the number of old white people who work at
| the company. I'm not saying this is true of all companies, but
| I think the issue is often that leadership pushes diversity in
| such a way that makes people feel excluded.
| boredumb wrote:
| GPT3 seems to disagree.
|
| can AI boost workplace diversity?
|
| `Yes, AI can boost workplace diversity in a number of ways. For
| example, AI can help identify unconscious bias in hiring
| practices, help managers develop more diverse teams, and support
| employees in feeling comfortable and included at work.`
| mjhay wrote:
| Amazon started using such a system a while back trained on their
| hiring data, and it started very heavily selecting against
| applications with female-indicating proxies, and was abandoned.
|
| In another case,
|
| _After an audit of the algorithm, the resume screening company
| found that the algorithm found two factors to be most indicative
| of job performance: their name was Jared, and whether they played
| high school lacrosse_
|
| which reads like a comical stereotype of a young male New England
| WASP; honestly kind of impressive.
| jerf wrote:
| In general, if you are a machine learning person and you are
| using a regression system that "explains" itself somehow, I
| highly recommend frequently looking at those explanations. Once
| you're excited that you've built a 90%+ model, crack open the
| hood of that model before you deploy it and do a sanity check
| on it. While ML algorithms can occasionally pluck some
| interesting insights out of the data, in my admittedly-limited
| experience I've also seen some models put together out of what
| is clearly just garbage, meaningless weights placed on
| attributes that can't possibly be _that_ relevant carving out a
| hyperplane through hyperspace that can 't possibly represent
| reality, despite all the efforts of ML models to hold out
| validation data and rotate their validation data and everything
| else to avoid overfitting.
| mjhay wrote:
| Once these systems get complicated enough, it gets really
| hard to do that. Unless you are doing linear models or tree
| ensembles, it can be really difficult to get a clear picture
| of variable importance/effects. It's one thing to extrapolate
| locally (fine for linear models), but that has little to do
| with anything relevant when there's lots of higher-order
| interactions.
| jerf wrote:
| "Once these systems get complicated enough, it gets really
| hard to do that."
|
| That's why I qualified it. Obviously if you're working in
| neural nets, you're not going to be cracking open that
| hood. But one of the dirty secrets of ML is you don't
| always need those techniques. There's the space of problems
| where linear regression and similar techniques are all you
| need, there's the class of problems where it doesn't matter
| what you throw at it you're not getting anything, and the
| "in between" where deep learning and such helps certainly
| has some _interesting_ problems in the space, but it isn 't
| necessarily the majority of problems you encounter in the
| wild.
|
| The scary thing is that not being able to crack open the
| hood doesn't mean the model isn't complete nonsense. It
| just means you can't examine it.
| RodgerTheGreat wrote:
| What is a safer assumption?
|
| - My model is accurate, for reasons I can't understand,
| verify, or externally justify
|
| - My model is not accurate
|
| edit: to be more clear on my stance, if a modeling
| technique is too difficult to justify or explain, it is not
| a good technique and should not be used, even if the
| results seem appealing.
| mjhay wrote:
| The second one. It's important to have a clear picture of
| biases and how your model could fail to generalize. In
| the case of social data from a deeply biased society,
| these issues can be seen a mile away.
| tyho wrote:
| The thing is, this is a laughable anecdote because clearly
| there is no causal relationship between lacrosse and job
| performance. But should a company care? Surely what matters
| most is whether a factor is predictive of job performance. We
| can assume that playing lacrosse was predictive, does it matter
| to Amazon if the relationship is causal?
|
| Obviously this doesn't take into account political pressures to
| achieve outcomes other than that hiring the candidate most
| likely to perform well.
| mjhay wrote:
| Selecting for lacrosse players (which as you say, has no
| causal relationship to job performance) necessarily selects
| against non-lacrosse players. Lacrosse players tend to be
| white and male,
|
| Amazon should care, due not only to obvious legal risk, but
| also because they are excluding candidates who otherwise do
| as well or better than a particular lacrosse player. Why
| would it make any kind of sense to artificially shrink their
| candidate pool?
|
| This is also a moral thing, not just a political thing. It's
| not right to discriminate against people for reasons other
| than job performance.
| commandlinefan wrote:
| > excluding candidates who otherwise do as well
|
| But the explicit, stated goal of diversity efforts is
| specifically to exclude candidates who otherwise might do
| well, but are not diverse, because non-diverse teams can't
| perform as well as diverse teams.
| hbrn wrote:
| Playing devil's advocate here:
|
| > Lacrosse players tend to be white and male
|
| I suspect they also tend to attend certain schools, and
| that could very well be causal.
|
| > Why would it make any kind of sense to artificially
| shrink their candidate pool?
|
| It doesn't make sense to shrink it if you have 10
| candidates. But if you have thousands of resumes, it's not
| shrinking, it's filtering.
|
| > It's not right to
|
| It's naive to expect big corporations to act morally. Best
| you can do is force them to act legally.
| Veuxdo wrote:
| Over-fitting.
| commandlinefan wrote:
| overfitting is a pretty well-understood and well-documented
| problem that we have statistical models to guard against -
| if this was just a simple case of overfitting, you'd think
| that Amazon of all organizations would be able to confirm
| and correct it.
| MichaelCollins wrote:
| There is a positive correlation between athletic ability and
| intellectual ability; strong bodies and strong minds
| correlate with each other (both are downstream of good
| nutrition and genetics.) Furthermore successful participation
| in team sports implies good teamwork and social skills.
| mannykannot wrote:
| > We can assume that playing lacrosse was predictive.
|
| I am not going to assume that without further investigation.
| I am rather skeptical of the robustness of predictive models
| in the absence of relevant causal relationships.
| mjburgess wrote:
| There's several ways of something not being causal. You're
| assuming here that there's a reliable proxy variable (eg.,
| wealth -> good education -> "Jared") -- which is unlikely to
| be the case. These proxies will disappear: poorer people will
| adopt those baby names.
|
| Whilst, yes, _everything else being equal_ some non-zero
| knowledge of reliable proxies is useful. But in a hiring
| process, not everything is equal. You can, eg., trivially
| examine the candidate.
|
| its trivial to get actually relevant data to hiring, making
| "AI" dangerously useless. Is substitutes a high-quality
| process (measurements of the actual applicants) for a low-
| quality process (random statistical properties of aggregates
| of existing employees).
|
| The story of "AI" over the last decade, it must be said.
| brightball wrote:
| > clearly there is no causal relationship between lacrosse
| and job performance
|
| IMO it could have an effect on "soft skills". Learning to
| have an authority push you to beyond your known limits.
| Working and training within a team to achieve a goal.
| Learning as a team to face and overcome weaknesses. Social
| skills that go with it. Humility from dealing with loss.
| Learning to celebrate victory with dignity.
|
| There's a lot of educational positives that come from team
| sports. It's certainly not the only place that a person can
| get those skills, but it's a very common one.
|
| IMO it's one of the biggest benefits of US football simply
| because the teams are so large, diverse and position groups
| have different but complimentary jobs.
| pseudalopex wrote:
| Does lacrosse teach those things better than other team
| sports?
| csydas wrote:
| I understand your point, but I disagree with it.
|
| Just like there are different versions of "good" leaders
| and "good" workers in any given field, there are different
| types of "good" in sports.
|
| Similarly, sports has a much more straight-forward rewards
| system; score a point, prevent the opponent from scoring,
| immediate result and reward.
|
| For anything technology related, the action => reward
| system tends to be far more delayed and requires a
| different approach and type of patience. Whether it's
| Devops, Programming, Administration, or Support, even if
| you do everything right, the reward might be delayed or
| sullied by negative rewards. (e.g., network is never fast
| enough for users, it's still too complex to use your
| program, the solution is just not desired by the user)
|
| Again I do understand the premise of your statement, and
| for some situations I can agree there is crossover, but my
| experience in hiring for technical positions is that sports
| participation has been a non-factor at best. Too often,
| it's been a negative one because the rewards for technology
| related challenges tend to be very delayed or difficult for
| the participant to recognize. Success in Technology is very
| personal and goes beyond the tangible result, and instead
| is a victory of the self as you grow intellectually and
| better understand something. This does happen in sports
| also (you train yourself to recognize openings that maybe
| didn't exist for you before or you couldn't act on due to
| physical limitations), but there are other rewards that
| make sports more compelling.
| golemiprague wrote:
| retrac wrote:
| > clearly there is no causal relationship between lacrosse
| and job performance
|
| There's almost certainly a correlation due to success in
| lacrosse having a lot of crossover with success generally. A
| young man who plays lacrosse is probably going to be
| physically healthier than average. It's also a very upper
| middle class sport, typically played by wealthier types. And
| children who grew up in wealthier households tend to be ...
| well, better at everything, really. Hockey is a great
| indicator of that here in Canada; the equipment is expensive.
|
| I wouldn't be surprised if it's causative, either. Sports may
| improve cognitive performance, and they tend to teach
| cooperation and leadership. Plenty of studies suggest it
| helps academics.
|
| If I felt free to discriminate and if I knew nothing else
| about a candidate other than whether they had played a
| physically-demanding and expensive team sport within the last
| year, I would hire the ones who had over the ones who had
| not. It'd skew heavily towards fit 20-40 something guys from
| middle-class backgrounds with college educations. Surprise!
| Probably what the algorithm picked up, indirectly. Of course,
| it also blatantly skews white, male, and economically elite.
| pfisherman wrote:
| I can't tell whether or not this is satire. Either way,
| this is utterly ridiculous.
|
| Now only hiring people from OxCam + Ivy + Stanford --
| that's the big brain move.
| retrac wrote:
| Not satire. Why do you consider it ridiculous?
| pfisherman wrote:
| Because it has about the same level of class and
| intellectual rigor as "hire a Jew because they are good
| with money" or "hire a chinaman because they are good
| with numbers".
|
| Also not a lot of white guys named Jared playing lacrosse
| at Oxford or Cambridge (or Peking University).
| retrac wrote:
| If I knew nothing other than whether someone was Jewish
| or not, and if I felt free to discriminate, I would, of
| course, hire the Jewish person. On average they do better
| than typical Americans across the board. In reality, I
| don't feel free to discriminate. So it's a problem if I
| know that a candidate is Jewish, because I'm clearly
| biased and working off a stereotype there. So is the AI.
| Maybe that helps clarify the domain my thought experiment
| was operating in.
| commandlinefan wrote:
| The promise of AI is that it will be able to find
| associations and figure out things that humans normally
| wouldn't - so the AI is now finding associations and
| figuring out things that humans previously didn't notice,
| and we're dismissing it.
| pfisherman wrote:
| Are any of these previously unnoticed associations? I
| think the problem here is that they are not typically
| informative or predictive of "success" -- whatever that
| means here. I don't need a model that is essentially a
| shitty heuristic (or collection of them) because I can
| get that for free - ex. "we only recruit / hire from the
| ivys".
| BlargMcLarg wrote:
| That's great, but you're still making assumptions. And
| that's the whole problem with diversity and anti-diversity
| both.
| retrac wrote:
| It's not an assumption that sports is associated with
| general success and intelligence. I felt it was well-
| established enough of a fact, that there's no need to
| link to studies showing that. It's even the basis for
| public policy in my country; we do sports-based
| interventions for troubled youth; such programs are one
| of the few things that seem to actually work to keep
| young men going to school and improve academics.
|
| Now if you mean I'm making an assumption about a
| candidate, based on nothing more than one weak indicator,
| well yes. That was the point of my thought experiment. If
| you only knew whether they played sports, while not a
| very good indicator, it's better than flipping a coin.
| Ultimately, that's what hiring is. You have some
| indicators like education and previous work experience,
| and you make an assumption based on those facts, none of
| which align 100% with how they're going to perform at the
| job you're hiring them for.
| lelanthran wrote:
| > It's not an assumption that sports is associated with
| general success and intelligence.
|
| No, it isn't. The "dumb jock" trope is widely known for a
| reason.
|
| Healthy body is associated with intelligence, and for
| good reason.
| BlargMcLarg wrote:
| >Now if you mean I'm making an assumption about a
| candidate
|
| That was even more obvious than the sports and cognition
| fact.
|
| Here's the thing: you're doing the exact thing that
| caused the problem in the first place. Creating thought
| experiments and narrating them at length regarding
| scenarios which are a rarity, given a problem which
| occurs frequently, in an environment with thousands of
| other variables.
|
| In a world where we do in fact have better indicators
| which are still not used, despite being well researched.
| We have plenty of options remaining before we need to be
| this nitpicky.
| retrac wrote:
| It's hard to imagine an indicator that wouldn't be
| closely correlated with family wealth and social status.
| Wealth and social status are the primary means to access
| the things that make a person more likely to be
| successful! Almost any indicator you pick will end up
| being a proxy for those, to some degree.
| [deleted]
| MisterBastahrd wrote:
| Here's a sport that is analogous to lacrosse in terms of
| racial makeup and financial demographics: golf.
|
| See the correlation now?
|
| Except that in golf's case, you usually see golf played
| around the country by rich white guys. Lacrosse is pretty
| much a northeastern thing, and when it's played elsewhere,
| it's normally at schools with the resources to host those
| clubs.
| randcraw wrote:
| If a vendor can't recognize extreme overfitting in their
| product, don't buy it.
| throw__away7391 wrote:
| Well obviously the solution here is to set aside a few
| million dollars a year of public money to send a dozen
| randomly selected children from low income households and
| disadvantaged backgrounds to learn to play lacrosse.
| permo-w wrote:
| > Surely what matters most is whether a factor is predictive
| of job performance
|
| not go all critical race theory, but is this all that
| matters? should there not be a place in society for people
| who have lesser aptitude for corporate work?
| LawTalkingGuy wrote:
| Yes, but should that place be performing work they aren't
| good at?
|
| Look for round holes before pounding a round peg into a
| square hole.
| boredumb wrote:
| We were only one paper away from the true revelation that
| female lacrosse players named Jared are by far the ideal new
| hires.
|
| hold on to your papers!
| bakugo wrote:
| Ah yes, the classic "we fed a bunch of real world data to an
| AI, said AI started finding patterns in the data and making
| decisions based on those patterns, so we shut it down because
| the patterns weren't politically correct" story. How many times
| have we heard this one?
| ok_dad wrote:
| Are you arguing people named Jared with high school lacrosse
| experience are the pinnacle of human achievement?
| bakugo wrote:
| I don't know about the Jared thing but I wouldn't be
| surprised at all if a high school sport ended up being a
| legitimate indicator of future job performance, even if not
| a very significant one.
| ok_dad wrote:
| I can agree that high school sports players are probably
| hard workers, sure. Clearly, hard work is a big part of
| doing well in life, since life has a lot of hard work
| (some of it is fun, too). However, you can't just blindly
| "follow the data" unless you have literally accounted for
| _all_ of the data that is currently not collected, which
| we don 't do because we cannot collect every metric
| relevant to this issue.
| fluoridation wrote:
| The statement verbatim was
|
| >the algorithm found two factors to be most indicative of
| job performance: their name was Jared, and whether they
| played high school lacrosse
|
| That doesn't mean that the "pinnacle of human achievement"
| necessarily is named Jared and played lacrosse in high
| school. It means that if you hire people who meet those
| conditions after filtering using X additional criteria, on
| average you'll get a more performant workforce than if you
| hire from the general population after filtering using X
| additional criteria.
|
| The algorithm found an objective truth. Given the data
| provided and the question asked, the answer should be
| indisputable. The only reason to reject it should be
| because someone thinks the answer is incorrect, but that
| means that either the algorithm is incorrect, the data is
| incorrect, or the question was poorly phrased.
| FormerBandmate wrote:
| The algorithm found that rich white preppy kids tend to
| perform better at a certain job, and then generalized
| that to say that you should hire them over other
| candidates. We don't know what that job was (for
| something like ultra-high-net-worth wealth management,
| that might be way more true then coding), and we don't
| know whether the metrics of performance used are
| applicable to anything besides how much the boss liked
| them
| ok_dad wrote:
| > The algorithm found an objective truth
|
| The "objective truth" for this specific data is that _out
| of all people hired_ dudes named jared and /or lacrosse
| players were more high achieving.
|
| That is where you get it wrong: the data is reflective of
| the bias in the current process, to remove that bias you
| can't simply reinforce the current behaviors via AI math-
| washing the same data.
| fluoridation wrote:
| That's silly. That would imply that the hiring process is
| actually very good at predicting performance, and it's
| biased towards hiring highly performant people named
| Jared and less performant people not named Jared, and
| it's _not_ hiring highly performant people not named
| Jared and less performant people named Jared.
| ok_dad wrote:
| What are you talking about? I was saying that the biases
| we have today in our hiring cannot be eliminated by
| analyzing that data and hiring the same types of people
| that are "high performance", because you'll be
| reinforcing the biases we have, rather than eliminating
| them.
| [deleted]
| PeterStuer wrote:
| I've been involved in hiring since te late 80's in the IT sector.
| The only constant that was there from the start is 'we need to
| hire more woman', which was understandable as less than 10% of CS
| degrees were female despite decades of positive discrimination,
| but every university department and every cmpany wanted to be
| perceived as 'female friendly'.
| narag wrote:
| Tell HN: RecruitME (YC 2023) chose Dr. Eliza and banned me for
| life out of global job market because of a deepfake FB photo.
| tigeba wrote:
| The authors of the paper do make a few concrete points about the
| problems with using AI to perform assessments on candidates. For
| example they mentioned that an AI designed to perform a Big Five
| assessment on a candidate might be meaningfully impacted by the
| candidate wearing glasses or having a bunch of books in the
| background.
|
| The vast majority of the critique is that to the extent
| anonymization works, it does not produce the outcome the authors
| desire. They explicitly ask for group based discrimination to be
| pre-baked into any sort of AI system to produce equity, not
| equality.
|
| "First, industry practitioners developing hiring AI technologies
| must shift from trying to correct individualized instances of
| "bias" to considering the broader inequalities that shape
| recruitment processes. Pratyusha Kalluri argues that AI experts
| should not focus on whether or not their technologies are
| technically fair but whether they are "shifting power" towards
| the marginalized (Kalluri, 2020). This requires aban- doning the
| "veneer of objectivity" that is grafted onto AI systems
| (Benjamin, 2019a, 2019b) so that technologists can better
| understand their implication--and that of the corporations within
| which they work--in the hiring process. For example,
| practitioners should engage with how the categories being used to
| sort, process, and categorize candidates may have historically
| harmed the individuals captured within them. They can then begin
| to problematize the assumptions about "gender" and "race" they
| are building into AI hiring tools even as they intend to strip
| racial and gender attributes out of recruitment."
| onos wrote:
| Further, it's important to compare the outcomes here to the
| counter factual, which must involve lots of individuals making
| decisions that are not based on explicitly coded up rules... ie
| rules that can be reviewed for bias and improved on
| collectively.
| alexpotato wrote:
| Orchestras had a male/female diversity problem until they started
| doing "blind" auditions where you could hear the music but not
| see the person playing the instrument. They even went so far as
| to reschedule an interview if they could figure out the gender
| based on something else e.g. heels clicking on the floor.
|
| The outcome? More women started appearing in traditionally male
| instruments positions e.g. the French horn which for a long time
| was considered a "man's instrument"
|
| You could do something similar in tech where one group performs
| the coding interview under supervision (to make sure it's
| actually the person to take the test) and another group reviews
| experience etc but without knowing the gender of the candidate.
|
| Granted, people may try to hack this system by using back
| channels etc but I would argue it's better than nothing.
|
| PS My favorite back channel approach was a college dean who have
| the same letter for all grad school reference letters but would
| use the student's first name for a positive reference vs last
| name for a negative reference.
|
| e.g. - "Susan is fantastic" == positive - "Miss Jones is
| fantastic" ==
| commandlinefan wrote:
| > You could do something similar in tech
|
| Honestly... why bother though? Whatever process you put in
| place to ensure diversity will be scrapped as a failure if it
| doesn't produce a very specific outcome (say, 50% men and 50%
| women). The only process that will be considered a success is
| one that leads to a particular outcome, so why not stop with
| the whole song and dance and just mandate the outcome and stop
| playing games with the process to get there?
| awb wrote:
| Removing bias and increasing diversity are two different
| goals.
| fsckboy wrote:
| he knows that, that's what he's saying
| awb wrote:
| I don't think so. They're saying, just mandate diversity,
| rather than trying to develop complex processes that are
| judged on how diverse their outcomes are. Which is a fair
| point.
|
| But, the piece I think OC is missing is that another
| worthy objective is to reduce bias, which may or may not
| lead to diverse outcomes. So, that's why it would still
| be worth doing something like a double-blind coding test
| if your objective is solely to reduce bias.
|
| If your objective is to boost diversity, then OC is right
| that a double-blind coding test is a complex route to get
| to that outcomes and that a different, more direct and
| explicit route might be better.
| haberman wrote:
| Blind orchestra auditions are a great idea that is sadly being
| called into question for not being progressive enough:
|
| https://www.nytimes.com/2020/07/16/arts/music/blind-audition...
|
| https://newmusicusa.org/nmbx/eyes-wide-shut-the-case-against...
|
| There is no clearer example of the divide between equality and
| equity, and the ways in which those two schools of thought are
| increasingly diverging.
|
| Equality: remove bias as much as possible
|
| Equity: intentionally introduce bias to achieve desired
| outcomes
| 8ytecoder wrote:
| I've been on the equity side of the fence for a long time
| now. My conviction comes from the fact that I (as very-low-
| but-not-poor middle class) got a chance to go to a
| "wealthier" high school and that made all the difference in
| my life. There's such a huge gap in confidence, persuasion,
| and even believing in what one can realistically achieve
| based on who you are surrounded with.
|
| My college on the other hand had a mix of students from all
| sorts of backgrounds and that's where I first hand saw the
| huge difference this makes. Even those with good grades came
| across as extremely lacking in confidence. A variety of other
| factors then adds up to completely mask their talent.
|
| And because there's not a single interview (at least in our
| industry) that doesn't look at your communication and other
| soft skills, these students mostly hung back and didn't get
| the same internship or jobs that I could get. And I was in
| the exact same social background as them.
|
| These differences are now extremely huge after about a decade
| and a half. That's why I'm convinced that comparing two
| people works well only when the platform they are operating
| on is comparable as well. Otherwise, you are not-quite-but-
| close-to comparing just the platforms/social strata.
| prottog wrote:
| I'm happy that the story played out well for you. Did you
| get that chance to go to the "wealthier" school from a
| process borne of equity, or one of equality? I'd imagine
| the latter is something like, you tested into and maybe got
| a scholarship for the school regardless of the means of
| your family, and the former something like, you were
| accepted into and maybe got a scholarship for the school
| regardless of your test score based on some immutable
| characteristic of your family.
|
| One of the core tenets of classical liberal thought is that
| children should not bear the sins of their fathers, and I
| think from that stems a lot of support for public schools
| acting as an equalizer between children of higher-achieving
| and lower-achieving parents; because whether on the
| equality side or the equity side, most people agree that
| being born to a poor family, or one that does not value
| education, should not determine the entire path of your
| life, start to finish.
|
| But your view seems too pessimistic; from your belief that
| two people cannot be compared unless they are coming from
| an equal footing, it stands to reason that you also believe
| that there are basically no individual differences, only
| group differences. And that individual effort matters
| little in light of their background.
|
| Should we hire as a salesperson someone who lacks
| confidence and interpersonal skills, or a mason someone who
| is physically weak? Sure, nobody chooses to have a stammer
| or be asthmatic as a child, and those differences do
| compound over time and you end up with someone who finds it
| hard to make a sale or lift a concrete block. And I believe
| in giving people a chance. But at some point, individual
| efforts should matter.
| [deleted]
| commandlinefan wrote:
| > I've been on the equity side of the fence for a long time
| ... I got a chance to go to a "wealthier" high school
|
| I've never seen an equity advocate recommend taking
| wealthiness of high school into account when measuring
| who's most deserving of equity, though.
| fsckboy wrote:
| you benefited from your spot in what you describe as a
| wealthy high school, and we read into that better teachers,
| more extra programs, etc. (not a challenge or a criticism)
| in general though (i.e. not in public school), taking a
| spot in a program denies that spot to somebody else. The
| human capacity for a sense of fairness and rooting for the
| underdog on the one hand says "great, it's nice that it
| happened for you!". But in the types of equity/equality
| decisions that are being made in general, what happened to
| the other person who didn't get the benefit you got? what
| if she was "better qualified" or "more deserving" or "would
| have benefited society at large more"?
| schemester wrote:
| That blind audition claim is actually pretty weak:
| https://www.wsj.com/articles/blind-spots-in-the-blind-auditi...
| Aransentin wrote:
| The "blind orchestra auditions" story gets repeated a lot, but
| the study it originates from did not find any statistically
| significant effect (and even so the ultimate outcome was that
| _men_ benefit from blind auditions, not women).
|
| http://jsmp.dk/posts/2019-05-12-blindauditions/
| tarboreus wrote:
| Thank Malcolm Gladwell for that one.
| ackfoobar wrote:
| > While women have a higher proportion advanced in several
| categories, they have a lower proportion advanced in the
| semifinals category.
|
| Do you think greater male variability (hypothesis) has to do
| with this?
| KaoruAoiShiho wrote:
| There was literally a big discussion on this 2 days ago.
|
| https://news.ycombinator.com/item?id=33168581
| Silverback_VII wrote:
| was Bobby Fisher right?
|
| Bobby Fisher on women in 1963: https://twitter.com/olimpiuurc
| an/status/969224340297302021?t...
| scarmig wrote:
| Andrew Gelman had a good post[0] about the blind orchestra
| auditions:
|
| > I think they're talking about the estimates of 0.011 +/-
| 0.013 and 0.006 +/- 0.013. To say that "the impact . . . is
| about 1 percentage point" . . . that's not right. The point
| here is not to pick on the authors for doing what everybody
| used to do, 20 years ago, but just to emphasize that we can't
| really trust these numbers.
|
| Along the same lines, one recruiting startup did a voice
| modulation experiment[1] to test the effects of masking gender:
|
| > After running the experiment, we ended up with some rather
| surprising results. Contrary to what we expected (and probably
| contrary to what you expected as well!), masking gender had no
| effect on interview performance with respect to any of the
| scoring criteria (would advance to next round, technical
| ability, problem solving ability). If anything, we started to
| notice some trends in the opposite direction of what we
| expected: for technical ability, it appeared that men who were
| modulated to sound like women did a bit better than unmodulated
| men and that women who were modulated to sound like men did a
| bit worse than unmodulated women. Though these trends weren't
| statistically significant, I am mentioning them because they
| were unexpected and definitely something to watch for as we
| collect more data.
|
| Australia did a study[2] where they hid gender on resumes for
| high level Australian Public Service positions:
|
| > What we found is that de-identifying applications at the
| shortlisting stage of recruitment does not appear to assist in
| promoting diversity in hiring. In fact, in the trial we found
| that overall, APS officers generally discriminated in favour of
| female and minority candidates. This suggests that the APS has
| been successful to some degree in efforts to promote awareness
| and support for diversity among senior staff. It also means
| that introducing de-identification of applications in such a
| context may have the unintended consequence of decreasing the
| number of female and minority candidates shortlisted for senior
| APS positions, setting back efforts to promote more diversity
| at the senior management levels in the public service.
|
| [0] https://statmodeling.stat.columbia.edu/2019/05/11/did-
| blind-...
|
| [1] https://blog.interviewing.io/we-built-voice-modulation-to-
| ma...
|
| [2]
| https://behaviouraleconomics.pmc.gov.au/sites/default/files/...
| collegeburner wrote:
| lupire wrote:
| You are telling on yourself a little here.
| collegeburner wrote:
| not sure what you mean? my only point is if there's an
| unconscious bias concern it's pretty hard to accurately
| address in situations where people don't just play music.
| ketzo wrote:
| meh, the way you're talking about the things that women
| do more comes off as pretty condescending
|
| "I feel" / "maybe we should" are not weasel words, they
| are phrases to start a conversation with an
| acknowledgement that you're not perfect
|
| "effusive praise for pedestrian situations" what does
| this even mean, different people speak more emphatically
| about things
|
| "don't get me started on emojis" why not?
|
| you're not saying anything hateful or whatever, but idk,
| if you were having this conversation with me in real
| life, I would come away with the impression that you do
| not take women very seriously
| Manuel_D wrote:
| > i noticed it was really easy for me to pick out that the
| one masked example was not a normal man, i'd have guessed
| either gay male or varbie gone too far.
|
| Perhaps that was because the article clearly labeled which
| one was the original woman's voice and which one was
| modulated to sound like a man? There's an easy way to test
| this: gave the interviewer guess the candidate's gender and
| see if they can guess consistently correct.
| collegeburner wrote:
| that's true but if you listen to the two samples it's very
| clear the voice is not a typical man. but agreed it would
| help to do a blind test and see if people could pick them
| out.
| UncleMeat wrote:
| "normal man" vs "gay male" is a bit worrisome here.
| collegeburner wrote:
| i mean "average man". the modulated example sounded a lot
| like the stereotypical "gay voice", how is that observation
| worrisome? since that voice is intentionally not like a
| regular man i don't see how it's problematic.
| pseudalopex wrote:
| Stereotypical gay voice is unintentional mostly and not
| reliable to predict sexual orientation.[1] Typical is the
| most neutral word for what you meant probably.
|
| [1] https://www.washingtonpost.com/news/wonk/wp/2015/07/2
| 8/what-...
| UncleMeat wrote:
| Gay people are normal.
| erenyeager wrote:
| What definition of normal do you operate on? Surely it's
| a minority of people, compared to the number of people
| who do not identify as gay. In sociology this is called
| social deviance, a feature of human variation
| pseudalopex wrote:
| Men are a minority of people. Being gay is more common
| than having green eyes. Social norms where most HN
| commenters live allow both. Social deviance means
| breaking social norms not being in a minority.
| collegeburner wrote:
| sure, not disputing that, i was using it as a synonym for
| "typical" or "common". that's a pretty common use of the
| word btw, google says it means "conforming to a standard;
| usual, typical, or expected"
|
| i also wasn't talking about gay people in general, only
| gay people who use the stereotypical voice which is a
| pretty small fraction of them. i think you're reading too
| much into nitpicking that word instead of the other three
| paragraphs.
| PhasmaFelis wrote:
| Yeah, and apparently "varbie" means "steroid-abusing female
| bodybuilder," so...yikes.
| collegeburner wrote:
| i mean if you listen to some of them that's the voice:
| woman's tone and inflection but pitched down closer to a
| man due to the effects of heavy androgen exposure. how's
| that "yikes"? the point is the modulated woman still
| sounded like a woman because there's more than pitch that
| sets mens/womens voices apart.
| JasonFruit wrote:
| I notice a lot of detracting responses here, but most aren't
| saying, "You're wrong." They're saying, "It's wrong of you to
| express these things unambiguously." Why is it that we let
| the urge to pretend gender and sexuality aren't real prevent
| us from addressing the problems we have with those matters?
| It's not an adult way to act.
| moosedev wrote:
| > if someone uses a woman emoji she is definitely a woman
| unless there's a specific reason to do so.
|
| I'm a man, and I sometimes use woman emojis because I like
| the look, or because it's fun. Do either of those count as "a
| specific reason to do so" in your framing? If not, consider
| how well your observations actually generalize to the world,
| and thus the universality of your statements.
| TazeTSchnitzel wrote:
| I think playing an instrument is easier to do this kind of
| interviewing for than software development. The latter is
| mostly about communication, whether it be by speech or writing.
| Those are things in which gendered signals are found all over
| the place.
| sam0x17 wrote:
| I would say it's actually the opposite. Most comms in
| software engineering is async/textual, and the work we
| produce is also textual. The takeaway here for me is maybe
| there is something to be said for doing an all-text
| interview.
| lupire wrote:
| Textual communication is inefficient in many collaborative
| scenarios. Humans speak faster than type.
| sam0x17 wrote:
| I find that verbal communication is often very
| inefficient in engineering scenarios. There is a reason
| RFCs are not done verbally.
| jason-phillips wrote:
| And yet my brain functions at least 100x faster than I
| can speak, leaving me to unsnarl the proverbial traffic
| jam in my head as the rate at which speech can
| communicate ideas lags behind the vast decision trees
| quickly generated in my thoughts.
|
| It's much more effective for me to write something that's
| both clear and persuasive than trying to express the same
| idea with synchronous, expository speech. For full
| disclosure, I was trained as a writer.
|
| Whenever the interview process includes a writing
| component, I invariably ace them. I'm a software
| engineer.
| whoooooo123 wrote:
| Also, if I want to know how good someone is at playing a
| musical instrument, I can usually get a good idea from just a
| few minutes or even seconds of hearing them play. There's no
| similarly fast way to judge how well someone will perform in
| a programming job. (Or at least if there was, hiring would be
| a _lot_ easier.)
| PolygonSheep wrote:
| My question is why get so fancy? Why not just use a racial/gender
| quota system? Just say "we want X% from demographics A, B, and C"
| and then hire the best you can from those groups and call it a
| day?
|
| That way you can just dial in the exact amount and types of
| diversity you want.
| LawTalkingGuy wrote:
| Because that sort of hiring produces less-qualified diversity
| hires. If you're running a for-profit business you're better
| off hiring from the largest pool possible.
|
| If you want to avoid the less-qualified you can either try
| harder to hire better, which is slow and expensive, or you can
| offer more, which is expensive but works.
|
| Hiring without regard for quality but paying the same seems
| like it would cement racist (or at best anti-diversity)
| feelings in the rest of the workforce and leave minorities
| without an available sense of accomplishment.
| RcouF1uZ4gsC wrote:
| One real danger is that there are a lot of data points that
| correlate with race and gender. Machine learning can become real
| good at inferring race and gender from what would appear to
| people to be unrelated variables but that, in fact have a small
| correlation. In the end, it is easy to use "AI" to whitewash your
| biases without explicitly targeting protected characteristics.
| seti0Cha wrote:
| This AI is being marketed to companies looking to improve their
| diversity. If it acts as you expect, then they just go out of
| business. I think the more likely scenario is that it achieves
| the desired outcome not by eliminating bias, but by inverting
| it, as that is more likely to result in measurable success from
| the customer perspective.
|
| My concern would really be that it ends up selecting for
| normalness. I could easily see it biasing against the homely,
| obese, people who struggle with mental illness, or the just
| plain weird. Would anybody check to make sure it wouldn't fail
| that way like they would with a more high profile bias like
| gender? Seems unlikely to me. I imagine some would even regard
| that as a positive feature.
| peyton wrote:
| Anything that doesn't produce the desired outcome is a
| nonstarter. There will always be some new group to advocate
| for. If such a group does not exist, one will be generated
| through intersectionality.
|
| That is the grift. These tools are a waste.
| robswc wrote:
| I will never forget. Some startup claimed they could find the
| best candidates for the job using "AI." They had a whole bunch of
| BS selling points. I think they even convinced a fair amount of
| companies to get on board too. I was thinking "how can people be
| so stupid!?"
|
| I then found one of the "features" was that they have the
| managers/bosses take the exam. They then offer a package that can
| weigh candidates based on correlation to their boss. Well, that's
| what it did. The marketing pitch was something something, view if
| they're compatible with your culture, something something.
|
| I then realized its genius. Charge a ton of money to do basic
| linear regression and spend 90% of your money on giving your
| clients an excuse to hire whoever they want without appearing
| bias. They would even appear "virtuous" because they let the "AI
| decide."
|
| Just awful, lol.
|
| Of course, I never got to see the internals and I don't want to
| slander but the "test the boss, correlate with new hire" was 100%
| real.
| z_zetetic_z wrote:
| How problematic. Not least because hiring for 'culture fit' is
| an anti-pattern, and orgs should be hiring for 'culture add'.
| tomp wrote:
| Well, that's literally what US colleges have been doing for
| decades. Basically outsourced IQ tests (since IQ tests during
| the hiring process are illegal).
|
| High time someone else got in on the action.
| MichaelCollins wrote:
| IQ tests during hiring are illegal _if_ that IQ test has a
| disparate impact against a protected class. (It turns out
| they pretty much all do, even those specifically designed not
| to, like Raven 's Progressive Matrices:
| https://en.wikipedia.org/wiki/Raven%27s_Progressive_Matrices)
|
| A "totally not an IQ test" IQ test is safer, particularly if
| it is bespoke, because new/obscure tests are less likely to
| have been studied, and consequently, any forbidden trends in
| test outcomes are less likely to be discovered.
| verisimilitudes wrote:
| How could that be, if we be all the same?
| sokoloff wrote:
| > IQ tests during hiring are illegal if that IQ test has a
| disparate impact against a protected class.
|
| Are you sure about that?
|
| That seems unreasonable and everything that I can find in 5
| minutes of Googling suggests that they are legal-but-
| restricted, to cases where the skill/aptitude being tested
| is relevant to the job being hired for. (You couldn't use
| an IQ test to hire a fry cook, as an example.)
| MichaelCollins wrote:
| Hiring practices which discriminate based on attributes
| that would otherwise be illegal to consider may be
| permitted if those attributes are Bona fide occupational
| qualifications (BFOQ.) For instance, blind people can't
| be bus drivers.
|
| IQ tests are verboten after Griggs v. Duke Power Co. As
| far as I'm aware, claiming a BFOQ exemption to this will
| fail, or at best, is unproven. IQ tests discriminate by
| race (even if unintentionally) and BFOQ exemptions don't
| apply to racial discrimination. Please correct me if I'm
| wrong.
| throwaway09223 wrote:
| This is really interesting to look at in the context of
| other hiring testing -- like Google's practice of asking
| silly abstract questions like "invert a binary tree" for
| jobs which do not involve substantial algorithmic work.
|
| eg: https://twitter.com/mxcl/status/608682016205344768
| sokoloff wrote:
| EEOC policy page:
| https://www.eeoc.gov/laws/guidance/employment-tests-and-
| sele...
|
| The use of tests and other selection procedures can be a
| very effective means of determining which applicants or
| employees are most qualified for a particular job.
| However, use of these tools can violate the federal anti-
| discrimination laws if an employer intentionally uses
| them to discriminate based on race, color, sex, national
| origin, religion, disability, or age (40 or older). Use
| of tests and other selection procedures can also violate
| the federal anti-discrimination laws if they
| disproportionately exclude people in a particular group
| by race, sex, or another covered basis, _unless the
| employer can justify the test or procedure under the
| law._
|
| That is not a categorical exclusion.
|
| Department of Labor Employers' Guide to Good Practice
| (2000, 29 years after Griggs):
| http://assessmentresources.pbworks.com/f/empTestAsse.pdf
|
| Similar language: One of the basic principles of the
| Uniform Guidelines is that it is unlawful to use a test
| or selection procedure that creates adverse impact,
| _unless justified_. Adverse impact occurs when there is a
| substantially different rate of selection in hiring,
| promotion, or other employment decisions that work to the
| disadvantage of members of a race, sex, or ethnic group.
| lmkg wrote:
| Your understanding is technically correct but missing
| some nuance which is the heart of the matter. It's true
| the use of an IQ test _can be justified_. The important
| thing is that the use of an IQ test _must be justified_.
|
| Due to the Supreme Court ruling, IQ tests are assumed to
| have a disparate impact. They are _by default_ assumed to
| be discriminatory unless proven otherwise. A company must
| either prove the lack of disparate impact, or demonstrate
| a plausible connection to work performance (beyond
| "smart = good").
|
| Most hiring practices are default OK, and a claim of
| discrimination must be proven in court. This hiring
| practice has already been decided in court, so it is
| default not-OK and must be defended. That difference is
| significant.
| sokoloff wrote:
| Indeed. They must be justified; they are not "verboten"
| as GP claimed.
| PathOfEclipse wrote:
| How do IQ tests discriminate by race? This is the first
| I've heard of it, and I don't believe it.
|
| https://www.quora.com/Are-IQ-tests-racist
|
| There's some great answers there that are summarized as
| "no they are not".
|
| I am guessing you mean IQ tests discriminate by race in
| the sense that racial groups exhibit disparate outcomes
| on the tests. According to critical race theory, all
| disparate outcome is caused by racism, which would make
| IQ tests racist. Critical race theory is 100% wrong, of
| course, as are all the related intellectual disciplines
| that make the same false and unfounded claims.
| Unfortunately, this "invincible fallacy", as the great
| philosopher Thomas Sowell calls it, really seems
| invincible to facts, truth, and reality. It is a virus on
| our culture and societal consciousness.
|
| IQ tests don't discriminate by race. They discriminate by
| IQ. That's literally the point of the test. Race has
| nothing to do with it.
| MichaelCollins wrote:
| > _How do IQ tests discriminate by race?_
|
| Like this:
|
| > _I am guessing you mean IQ tests discriminate by race
| in the sense that racial groups exhibit disparate
| outcomes on the tests_
|
| If you go to court trying to argue that your hiring and
| promotion system isn't racially biased and it merely
| exposed the truth of some racial groups being less
| intelligent than others, you're going to lose. It doesn't
| matter if you're factually correct, you're going to lose
| anyway and you'll probably be personally ruined for even
| attempting it.
| steve76 wrote:
| throwaway4993 wrote:
| Huh? What are you referring to? Students? Faculty? I've been
| both and haven't seen this ever. Or are you trying to claim
| that the SAT and GRE are IQ tests?
| thaumasiotes wrote:
| The SAT and the GRE are IQ tests. They have the same
| psychometric characteristics and reveal the same
| information about testees. They correlate with IQ tests at
| the same level that different IQ tests correlate with each
| other. Results from IQ tests explain very large amounts of
| the variance in SAT and GRE scores, and vice versa.
|
| This is not a strictly necessary state of affairs - if
| large numbers of people who knew nothing about chemistry
| started taking the chemistry GRE, the strongest signal
| provided by the chemistry GRE would switch from "IQ" to
| "whether or not the person has ever studied chemistry".
|
| But (1) the test would remain an IQ test once you ignored
| the very low scores of people who had no reason to take the
| test; and (2) in reality, people who have never studied
| chemistry don't take the chemistry GRE.
| mistrial9 wrote:
| a PhD scientist in California was awarded a multi-year $5m USD
| grant based on similar principles, around 2015, through the
| University of California. I sat through her lunch talk to a
| group of researchers. The merit of the idea was that she could
| find under-represented demographics of young potential coders
| by matching various online activity with their resume, and
| assuming a lot of low-income education credits are not going to
| look very good on a typical boilerplate resume. I was mildly
| annoyed at the time.
| robswc wrote:
| Wow.
|
| Yea. I think the hiring processes is broken in a lot of ways.
| I also think trying to get AI to solve it or adding even more
| layers into the process is only going to make things worse.
| tikhonj wrote:
| There's a qualitative difference between using AI for
| _sourcing_ --that is, finding candidates you would not find
| normally--and using it for _assessment_.
|
| The impact of a biased source is attenuated by having other
| sources and by the rest of your assessment process. Perhaps
| you end up with a marginally "worse" pool of candidates at
| the top of your hiring funnel... but, frankly, it's already
| hard to do much worse than the mix of LinkedIn keyword
| searches, resume screens and ad hoc channels teams use today.
| Crucially, if this happens, the impact on any given candidate
| is minimal--a bit more competition that, if the rest of the
| process is any good, should be negligible.
|
| The impact of bias in how you _reject_ candidates, on the
| other hand, fundamentally _cannot_ be compensated for in the
| remaining process. A rejected candidate is already out! Any
| bias in rejecting steps is going to be directly reflected in
| the sort of teams you can recruit.
|
| Mixing together sourcing methods that are biased in different
| ways can cancel out the bias. Mixing together assessment
| steps that are biased in different ways compounds the bias.
|
| Traditional resumes are pretty awful any way you look at
| it[1] and developing new ways of sourcing qualified
| candidates that get overlooked by the traditional approach
| doesn't create the sorts of problems that new assessment
| methods do, so the research you're describing seems
| unambiguously useful.
|
| [1]: interviewing.io did a cool study looking at how resumes
| compare against the mock interview performance of users on
| their site. They just don't work well. This isn't a peer-
| reviewed study, of course, but, frankly, the data and
| approach are _more_ compelling than some of the peer-reviewed
| research I 've seen on the subject.
|
| https://blog.interviewing.io/resumes-suck-heres-the-data/
| BlargMcLarg wrote:
| Traditional resumes being useless has been pretty well
| researched for decades. Hiring is just stubborn and slow.
| mistrial9 wrote:
| you make a couple of good points, regarding sourcing, and
| hint at compounding effects, which is probably a very deep
| topic here. I tipped my hand by indicating I was mildly
| annoyed, and failed to differentiate between perceived
| positives and negatives in the process. So I will try to
| fill that in a bit..
|
| Let's start with aptitude/talent of the candidate.
| Certainly those with aptitude and/or talent might be hidden
| in the modern floods of resumes. People _lie_ about skills,
| and supporting "automated investigation" might refute or
| amplify assertions. People also miss critical elements in
| describing themselves, so something similar would apply
| there. Your point that this kind of automation might
| _increase_ pools of candidates, and thereby making a chance
| for some people currently overlooked, is consistent with
| the merit found in the research grant.
|
| Is that the whole story however? Is there a shortage of
| skilled people doing paid computer activity? It is no
| secret that wage negotiation is ongoing and will
| essentially never be solved. Individual people grow and
| change, especially in the second decade of life. Industry
| conditions change, especially with web-related work. And,
| the industry itself is famously seeking bargains.
| Outsourcing is a constant on the Internet. There are many
| more people than jobs at any given time, but most people
| are not great fits or even able to do a job. So.. how to
| draw some conclusions there, about where "automated
| investigation and classification of human beings as
| workers" might fit in.
|
| It is literally true that some large-scale business seeks
| to make replaceable work roles such that low-skill, low-
| commitment people can be replaced in large numbers on any
| given week. Wages and benefits can be cut and sometimes
| aggressively cut, amidst that churn. Is there a shortage of
| college-educated adults who are willing to participate as
| "churn" elements, also known as gig-work? Perhaps, but for
| stable social lives, reliable income, stability to raise a
| family, retirement and medical benefits, gig-work acts like
| a chemical solvent, literally dissolving the bonds,
| seniority and commitments needed for longer term stability
| to grow. More comments about wage working in the USA and
| elsewhere could be made, but overall this part would fit
| into the "uses of the technology" side, not the technology
| itself per se.
|
| Another angle worth mentioning is the difference between
| developing skills in a workplace over time, creating the
| environment that sustains skill development, and seeing the
| benefits of skill development return to the skill holder,
| rather than the corporation. This worker-side of worklife
| historically overlaps mentors, ranking, seniority and self-
| determination of the skill holder. Almost none of the
| "automated investigation" lends itself to this side, at
| first appraisel. If there is someone who has unfound skills
| in rigorous or rare aptitudes, there would be some context
| to that since it takes time to grow stronger. That sort of
| thing would almost certainly be included in a resume.
|
| Overall, I claim that the "automated investigation" of
| employee aptitude strongly aids a "race to the bottom" of
| lowest wage for highest skill, and does almost nothing to
| build and retain skill over time by the skill holder. My
| reason for being "mildly annoyed" probably has more to do
| with systematically declining to engage mid-level skilled
| workers in mid-life in mid- to high-cost of living
| societies, and not so much to do with finding and engaging
| people who are overlooked by the current system. Since I do
| not have the problem of thousands of over-stated resumes
| for ordinary roles to filter through, the filtering side of
| "refuting false claims on resumes" is not a problem for me,
| though this technology is certainly primed for that.
| humanistbot wrote:
| Sounds like https://pymetrics.ai ?
| robswc wrote:
| Looks _very_ close, if not it.
|
| It has honestly been so long I can't say for sure. It was
| early 2010s.
|
| Seems a lot of the marketing material is vague and you need
| to request a demo. Looks pretty silly, regardless. Play a
| bunch of "games" as part of the interview? I bet HR loves it
| tho, lol.
| duxup wrote:
| At least as far as technical knowledge goes... that maybe works
| pretty well.
| robswc wrote:
| I mean, it works incredibly well if you're a boss and want to
| hire a bunch of clones... but if you're a bad boss... lol
| gojomo wrote:
| Can anything save a bad boss?
|
| Maybe every boss deserves to be matched with the team that
| makes all involved most-likely to mutually-succeed, even if
| it's a peculiar & unrepresentative bunch of people who can
| only work with each other.
|
| And further, if that best-effort assemblage still
| consistently malfunctions, the boss, & the whole team,
| should be encouraged to find other roles and teams that
| have a better chance of succeeding.
| robswc wrote:
| I totally agree. I just feel like a bad boss hiring a
| bunch of clones would only be worse for the company.
| duxup wrote:
| Is suspect that is often the case.
|
| Is it optimal? Nope. But if you're offering candidates /
| filtering them for someone... you give them what they want
| / like right?
| thedracle wrote:
| HireVue AI Assessment.
| fallingknife wrote:
| > I don't want to slander
|
| It is never slander to speak the truth
| cercatrova wrote:
| Except in Japan where you can be prosecuted even if what you
| spoke about someone else was true. They see it as destroying
| the their person's honor.
| aliqot wrote:
| For the other 99.87% of us on this board, this is good
| info.
| cercatrova wrote:
| Depends on your jurisdiction, really. Even in Western
| countries the laws differ on what is slander and what
| isn't.
| hutzlibu wrote:
| It is a very grey area. In generally you can say anything
| you can proof to be true. But are you prepared to uphold
| your proof against a legion of expensive lawers in court?
|
| (what is truth after all)
|
| So if you loose, things can get ugly, so most people try
| to be on the safe side and keep their mouths rather shut
| (except for pseudoanonymous online rants).
| fsckboy wrote:
| he didn't mean "I don't want to _technical-meaning-of-
| slander_ ", he meant "I don't want to badmouth perhaps
| unfairly since I probably don't have all the facts"
| robswc wrote:
| True. I guess I'd rather not create drama for drawing
| implications from marketing material, haha.
| fallingknife wrote:
| Fair enough, though I think it's pretty fair to assume that
| the truth is either the same or worse than the marketing
| material!
| jcranmer wrote:
| That's not entirely true. "Mr. Smith goes to the brothel
| every Tuesday" can be defamatory yet true since it omits the
| crucial details that a) Mr. Smith is the garbageman and b)
| Tuesday is garbage collection day at the brothel. Of course,
| the defamatory aspect is that you are very clearly implying a
| false statement, and if the implied statement were true, it
| would of course fail to be defamatory.
| nonethewiser wrote:
| > It is never slander to speak the truth
|
| It is never slander to speak honestly. Even if you're wrong,
| if you believe it, it's not slander.
| salawat wrote:
| The term is "math-washing". It's use as a means to facilitate
| accountability laundering has been a sticking point for a
| while.
| bheadmaster wrote:
| The excuse used to be "it's God's will". Now it's "it's AI's
| will".
| [deleted]
| [deleted]
| jslaD wrote:
| Claims workplace diversity is desirable are "spurious and
| dangerous"
| lm28469 wrote:
| Claiming it's undesirable equally is.
|
| There are plenty of good arguments in favor of workplace
| diversity, "forced diversity" or "AI enabled diversity" aren't
| part of them though
| tqi wrote:
| > They say it is a dangerous example of 'technosolutionism':
| turning to technology to provide quick fixes for deep-rooted
| discrimination issues that require investment and changes to
| company culture.
|
| I definitely agree with this sentiment, however I think it's also
| important to remember how bad human reviewers are. I think a lot
| of times, because it is harder to collect data measuring the
| effect of human systems, we focus on how bad AI systems are. At
| least with an algorithm you can:
|
| - try to identify biases via testing
|
| - back test changes to see how those biases change
|
| I think given the choice between equally biased human and
| algorithmic systems, I would have more faith that the algorithmic
| system could be meaningfully changed in the near term than a
| human system.
| calibas wrote:
| AI/ML is a poorly understood, but very well-hyped technology.
| Much like blockchain, it's being falsely promoted as a kind of
| panacea for all of mankind's problems.
| totorovirus wrote:
| Let's face it. We all know that being WASPy is the probably the
| best indicator of performance.
| daoist_shaman wrote:
| AI is probably the only thing that will be able to help humanity
| clean up the mess that it's still noisily making. I'm talking
| about real AI, not the Mickey Mouse data science buzzword that
| keeps getting promulgated by C-suite crayon eaters.
|
| Doomsayers can stir up all the FUD they want about AI, and point
| to works of fiction as their gospel. I, on the other hand,
| welcome a future where we are fairly governed and managed by our
| AI overlords, rather than exploited by "fellow" man.
| nsxwolf wrote:
| Not sure how this helps me get the candidates in the first place.
| Something's filtering non-white-male, non-asian-male candidates
| before they even apply to our organization.
| rsynnott wrote:
| Bad job ads? I believe there was some data showing that the
| "10x rockstar bla bla" style of ads tended to put off
| underrepresented minority candidates, in particular.
| robswc wrote:
| Might not be something to complain about, lol
|
| The "10x rockstar" JDs usually mean "we don't actually know
| what we want."
|
| Best, most senior job _and_ with best TC I ever got was just
| half a page saying exactly what they needed.
| almenon wrote:
| Only ~13% of CS grads are women last time I checked. So yes,
| the filtering is happening, and it's before the candidate even
| looks at the job advertisement.
| Manuel_D wrote:
| Is something filtering out Asian basketball players before they
| reach the NBA? Or may they just don't decide to become
| professional basketball players. Likewise among children who
| say they're interested in STEM, boys outnumber girls 3:1 as
| early as middle school [1]. The evidence doesn't indicate that
| they're being filtered out, they're not interested in the field
| in the first place.
|
| https://www.govtech.com/education/female-interest-in-stem-ed...
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