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Claims AI can boost workplace diversity are ‘spurious and dangerous’

• slavboj 4y ago
  You don't need "AI" to deploy the cutting edge "hire the black fella" algorithm.
• alexpotato 4y ago
  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" ==
  • TazeTSchnitzel 4y ago
    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 4y ago
      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 4y ago
        Textual communication is inefficient in many collaborative scenarios. Humans speak faster than type.
        • jason-phillips 4y ago
          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.
        • sam0x17 4y ago
          I find that verbal communication is often very inefficient in engineering scenarios. There is a reason RFCs are not done verbally.
    • whoooooo123 4y ago
      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.)
  • schemester 4y ago
    That blind audition claim is actually pretty weak: https://www.wsj.com/articles/blind-spots-in-the-blind-audition-study-11571599303 https://www.wsj.com/articles/blind-spots-in-the-blind-auditi...
  • KaoruAoiShiho 4y ago
    There was literally a big discussion on this 2 days ago. https://news.ycombinator.com/item?id=33168581 https://news.ycombinator.com/item?id=33168581
    • Silverback_VII 4y ago
      was Bobby Fisher right? Bobby Fisher on women in 1963: https://twitter.com/olimpiuurcan/status/969224340297302021?t=JrFNf3_m-cDs3fBOrGFBiA&s=19 https://twitter.com/olimpiuurcan/status/969224340297302021?t...
  • collegeburner 4y ago
    • lupire 4y ago
      You are telling on yourself a little here.
      • collegeburner 4y ago
        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 4y ago
          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
    • UncleMeat 4y ago
      "normal man" vs "gay male" is a bit worrisome here.
      • collegeburner 4y ago
        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.
        • 4y ago
  • Aransentin 4y ago
    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/ http://jsmp.dk/posts/2019-05-12-blindauditions/
    • tarboreus 4y ago
      Thank Malcolm Gladwell for that one.
    • ackfoobar 4y ago
      > 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?
  • commandlinefan 4y ago
    > 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 4y ago
      Removing bias and increasing diversity are two different goals.
      • fsckboy 4y ago
        he knows that, that's what he's saying
        • awb 4y ago
          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.
          • fsckboy 4y ago
            you're saying "do these things to get better" and he's saying "doesn't matter because": the people who most frequently say they are against bias and they are in favor of diversity in fact don't care about either, what they care about is evening out the employed %ages of (choose euphemism) underrepresented, vulnerable, at risk, traditionally overlooked, etc. etc., with no regard for measuring qualification, skill, productivity, preparedness, etc., and they also want equal pay regardless of the value of the work being performed. (people who become wealthy generally have skills that are highly leveraged over many customers, so blockbuster films, popular music, ubiquitous cellphones, disruptive X, etc. But shouldn't childcare workers earn as much? Our children are precious, our children are the future... but children need full-time a very high adult-to-child care ratio, and looking at traditional societies essentially half the population has made excellent childcare workers and the other half has been willing and able to do a bang-up job in a pinch, so while it's vital to each child, it's not exactly a skilled task, and will never command a high salary because there isn't money to pay for it)
  • haberman 4y ago
    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-auditions-orchestras-race.html https://www.nytimes.com/2020/07/16/arts/music/blind-audition... https://newmusicusa.org/nmbx/eyes-wide-shut-the-case-against-blind-auditions/ 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 4y ago
      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.
      • fsckboy 4y ago
        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"?
  • scarmig 4y ago
    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-orchestra-auditions-really-benefit-women/ https://statmodeling.stat.columbia.edu/2019/05/11/did-blind-... [1] https://blog.interviewing.io/we-built-voice-modulation-to-mask-gender-in-technical-interviews-heres-what-happened/ https://blog.interviewing.io/we-built-voice-modulation-to-ma... [2] https://behaviouraleconomics.pmc.gov.au/sites/default/files/projects/unconscious-bias.pdf https://behaviouraleconomics.pmc.gov.au/sites/default/files/...
• nsxwolf 4y ago
  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 4y ago
    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 4y ago
      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 4y ago
    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 4y ago
    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-education-and-careers-decreasing.html https://www.govtech.com/education/female-interest-in-stem-ed...
    • pseudalopex 4y ago
      The strongest plausible interpretation of their comment would count interest as a filter. 13 to 17 is older than middle school mostly. Preference at those ages is not evidence against social factors. The article said children lose interest in STEM careers as they get older. It referred to an MIT survey that found not having mentors or role models was a reason.
• spoonjim 4y ago
  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.
  • automatic6131 4y ago
    >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.
    • spoonjim 4y ago
      I don't see the difference. The sentences are equivalent to me except to denote a stock vs. a flow.
  • whoooooo123 4y ago
    Ask James Damore how well that went for him.
  • neon_electro 4y ago
    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 4y ago
      That's the argument that they make, duplicitously.
    • LawTalkingGuy 4y ago
      > Do you think that's why folks are looking to add diversity to the workplace? No, but they say obviously untested things like this: "diverse companies are more profitable and more likely to achieve long-term growth as an entity and in their industry." -- Marketwatch If that was true you wouldn't see the push for it, companies would be fine with their competitors missing the boat while they hired all the undervalued diversity. I would prefer honest messaging such as "Diversity may cost in the short term but we believe it is morally worth doing". Then we could have an open conversation about goals and methods and costs.
• narag 4y ago
  Tell HN: RecruitME (YC 2023) chose Dr. Eliza and banned me for life out of global job market because of a deepfake FB photo.
• robswc 4y ago
  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.
  • duxup 4y ago
    At least as far as technical knowledge goes... that maybe works pretty well.
    • robswc 4y ago
      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
      • duxup 4y ago
        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?
      • gojomo 4y ago
        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.
• jslaD 4y ago
  Claims workplace diversity is desirable are “spurious and dangerous”
  • lm28469 4y ago
    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
• mjhay 4y ago
  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 4y ago
    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 4y ago
      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.
      • RodgerTheGreat 4y ago
        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.
• RcouF1uZ4gsC 4y ago
  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.
  • junofan 4y ago
    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.
  • seti0Cha 4y ago
    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.
• jdmtheNth 4y ago
  Just identify the AI as your obligatory diversity if questioned.
• dangerwill 4y ago
  • fallingknife 4y ago
    73% of Americans oppose affirmative action https://www.pewresearch.org/fact-tank/2019/02/25/most-americans-say-colleges-should-not-consider-race-or-ethnicity-in-admissions/ 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_Proposition_209_Affirmative_Action_Amendment_(2020) https://ballotpedia.org/California_Proposition_16,_Repeal_Pr... Opposing racial discrimination is moderate. You are, in fact, very much in the minority here.
  • lm28469 4y ago
    Hire everyone you want, just don't use a shady """AI""" to tell you who to hire.
  • deleted 4y ago
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• lm28469 4y ago
  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 4y ago
    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 4y ago
      White people who don't want to date black people and lesbians who don't want to have sex with transsexuals already suffer this.
• PeterStuer 4y ago
  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'.
• sublimefire 4y ago
  Was covered in Register as well https://www.theregister.com/2022/10/13/ai_recruitment_software_diversity/ https://www.theregister.com/2022/10/13/ai_recruitment_softwa... if you feel the need for a bit more sarcasm and some interesting comments :)
• deleted 4y ago
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• tigeba 4y ago
  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 4y ago
    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.
• sbf501 4y ago
  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.
  • kypro 4y ago
    > "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.
  • Manuel_D 4y ago
    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.
• boredumb 4y ago
  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.`
• tomp 4y ago
• tqi 4y ago
  > 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.
• not2b 4y ago
  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.
• PolygonSheep 4y ago
  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 4y ago
    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.
• throwaway4993 4y ago
  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.
• calibas 4y ago
  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 4y ago
  Let's face it. We all know that being WASPy is the probably the best indicator of performance.
• daoist_shaman 4y ago
  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.
  • Sateeshm 4y ago
    Have you seen raised by wolves? Season 2 deals with this
• deleted 4y ago
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