23 ms·

Alexandria Ocasio-Cortez Is Absolutely Right About Racist Algorithms

• kauffj 8y ago
  There is no design of AI that can be simultaneously utilitarian, procedurally fair, and representatively fair. I'm going to repeat this again, since many people struggle with this. It is _literally impossible_ to achieve the best utilitarian outcome, the most procedurally fair outcome, and a representatively fair outcome. Anyone designing algorithms will have to make tradeoffs along this frontier. For a far better argument for the above than I can make in a Hacker News thread, please see these slides from Chris Stucchio: https://www.chrisstucchio.com/pubs/slides/crunchconf_2018/slides.pdf https://www.chrisstucchio.com/pubs/slides/crunchconf_2018/sl...
  • mehrdadn 8y ago
    This sounds an awful lot like voting impossibility theorems (Arrow's and its generalizations)... are they related?
    • natalyarostova 8y ago
      I don't think so. This is simpler, it's just stating that an optimization problem on a constrained information set must be weakly dominated by the full information set. Arrow's theorem is a more domain specific and elegant proof regarding political properties that can be simultaneously held.
    • hannasanarion 8y ago
      More like the orthogonality thesis, I think. These different value axes are totally unrelated, and building an algorithm that maximizes one does not necessarily mean that it maximizes the others as well, and this mismatch can have disastrous consequences, a la "The Paperclip Machine"
  • sam0x17 8y ago
    But it is possible to make one that is representatively fair first and foremost, with procedurally fair and utilitarianism at 2nd and 3rd priorities.
    • kauffj 8y ago
      Yes, it is. But if you're designing an algorithm that assesses credit risk and you do this, you will lose money compared to a company that does not do this.
      • chrisco255 8y ago
        You might even cause a financial crisis along the lines of the 2008 disaster.
        • toomuchtodo 8y ago
          As long as you're too big to fail and get bailed out, it's someone else's problem. EDIT: I didn't say this was just or right, just the current state.
      • sangnoir 8y ago
        The cost-to-benefit analysis of wheelchair ramps doesn't look great either (compared to not building one). Focusing on profit alone leads to some pretty terrible outcomes for people.
        • swsieber 8y ago
          Taking credit when you can't pay it back doesn't just hurt the bank - it hurts you too. Getting loaned money is not a right, and if you are irresponsible, it's harmful. So if lending more to a certain segment of the population hurts the bank's profit, I'd also expect it to cause more problems for that population segment too.
      • sam0x17 8y ago
        Which is why we need regulation forcing you to do it
  • dvtrn 8y ago
    I was interested in the slide deck and wanted to hear the author expand on some of his thoughts, found a video of his presentation if anyone else is interested. https://www.youtube.com/watch?v=Zn7oWIhFffs https://www.youtube.com/watch?v=Zn7oWIhFffs
    • Balgair 8y ago
      Wow, this is really good. I hadn't thought of some of these things and he exposes some points that should have been obvious to me. For example: You aren't guaranteed to be able to maximize two functions at the same time.
  • shadowbained 8y ago
    The problem here is, we don't even know what "fair" means. What do we mean by fair? Say we want to base fair on group outcomes, in which ways should we divide people into groups in order to perform some sort of balancing? Is hair color or texture a relevant way of subdividing people for instance? Are we being "fair" to red haired people? What if red haired people for some reason seem to be less likely to pay back loans? Will we need to adjust our credit rating model to bump the credit scores of red haired people slightly? Will we need to agonize over other features fed to our model to ensure that they can't be used as a proxy for red hair? Maybe???? But it seems very dishonest. I think, if anything, the models are probably too true for our tastes as social creatures who evolved to avoid social conflict (being 100% honest with your unruly neighbors can lead to a loss of harmony that is much more damaging than just putting up with some disturbances). Our idea of "unbiasing" the models is more likely actually making them biased in a way that pleases our monkey brains.
    • simion314 8y ago
      Maybe don't split groups on genetics/biology/birth. Also make sure the data you use is not a proxy for the above criteria
      • Iolaum 8y ago
        Easier said than done.
        • simion314 8y ago
          I know, this is why all this algorithms should not be hidden/proprietary. At least we have a chance to spot why/how the computer decided my fate.
          • hnaccount141 8y ago
            The problem is that in many cases (especially with deep learning), "the algorithm" is essentially a black box. There often isn't a straightforward way to determine exactly what the algorithm is actually learning.
  • benj111 8y ago
    I wouldn't say its impossible. They may happen to align, you cant force it though.
    • EpicEng 8y ago
      If there exists a scenario where it fails then yes, it is impossible. Just because something works the way you intend it to in some specific case doesn't mean it works in the general case (and the general case is what matters here.)
      • benj111 8y ago
        If it works in a specific case it isn't impossible. I agree that its the general case that matters. Impossible is a very strong word though. How wide do you want to cast the 'general' net? You can still have limited scenarios that are useful. You don't have to model the entire universe.
        • EpicEng 8y ago
          The GP's claim is that yes, it is _literally_ impossible. He cites a source[1]. If you have a disagreement with that theorem then by all means, share. Right now you're just saying "Nah I don't think so", which isn't very convincing. [1]: https://www.chrisstucchio.com/pubs/slides/crunchconf_2018/slides.pdf https://www.chrisstucchio.com/pubs/slides/crunchconf_2018/sl...
          • benj111 8y ago
            That article gives an example of the Scotsman and Englishman. Each are equal, 25% get 1200 on the sat, there is no allocative harm. It is therefore fair, no? If it is _literally impossible_ I must be misunderstanding something. Because I don't see the things he's mentioning as being mutually exclusive.
  • tboyd47 8y ago
    > There is no design of AI that can be simultaneously utilitarian, procedurally fair, and representatively fair. AI is just tools of humans. Any system that can be achieved with humans can also be achieved with AI. These AIs take us backwards from our existing human systems in addition to being much harder to correct later. The supermarket theft example in the slides you linked to is a prime example. In what store in America would that kind of display be deemed acceptable? Yeah, you save a few dollars a day on theft, but what kind of goodwill are you spending? Now that AI is here, people think they can get away with things like that because numbers.
    • gambler 8y ago
      > Any system that can be achieved with humans can also be achieved with AI. I hope you meant it the other way around.
      • dragonwriter 8y ago
        I think it's a rejection of “AI can't be racist”—if humans can be racist, the AI systems they employ can also be racist.
      • tboyd47 8y ago
        What I'm trying to say is that inasmuch as you consider the current legal and cultural environment of America to be utilitarian, procedurally fair, and representatively fair, any system that could be built using AI can achieve the same. I don't believe that they will a priori deliver an improvement. But there's no excuse for AI systems to show any discriminatory biases that have already been minimized in human systems via legislation, training, etc. Don't misunderstand me; I'm not saying that discrimination doesn't exist or that we live in a society with no biases. I'm saying there is no valid reason why AI should take us backward in that regard. Computers are extensions of the will of man. Our AI researchers and developers should ask themselves what kind of society they want to create. Whether they choose to turn a blind eye to discrimination and bias, or choose to actively combat it, then that will determine the result. Civil rights didn't just happen; there were people fighting for it actively. The same has to occur in the digital world if the same result is desired.
  • deleted 8y ago
    [deleted]
  • tomp 8y ago
    More to the point, “procedural fairness” or “equality of opportunity” (if I understand it correctly) is directly opposed to “representative fairness” or “equality of outcome”. In many situations, prioritising one will compromise the other. As an example, Harvard admission of Asian students. Perspectives differ on which one is more important. Personally, I’m absolutely for the former, but it increasingly seems I’m a part of the minority (or the silent majority).
    • 50656E6973 8y ago
      Equality of outcome seems like a mechanistic concept of the industrial age of standardized mass production. Equality of opportunity seems much more organic/humanistic because it allows for the natural variation in people's values and personality
      • SolaceQuantum 8y ago
        I am also interested in equality of opportunity- but how do I measure that everyone has an equal opportunity to achieve something? How do I measure the opportunity to consume nutritious food? The opportunity for access to the same healthcare?
        • yourbandsucks 8y ago
          It goes deeper than that if you go into cultural biases prevalent in poor (regardless of race) communities. Cigarettes, scratch-offs, crappy food.. what counts as opportunity? Is being raised a certain way tantamount to having 'less opportunity' to make better micro-decisions?
      • rtkwe 8y ago
        The problem is equality of opportunity is it's invariably applied in a way that ignores some very racist and uneven starting conditions and the effects of the past. Consider as a small example how schools are funded in the US, which is largely by local property taxes, so poorer places have poorer schools. These areas are heavily skewed towards minority populations because of some extremely racist issues in our past like redlining which kept blacks and other minorities out of the housing boom which drove a lot of middle class wealth growth through the 60s to 00s. So we build on this cycle generation after generation where kids in poor areas go to poorly funded schools and experience poor educational outcomes. Even if we solve the funding problem it's still not actually equal opportunity because the poorer kids don't get the same support outside of school due to parents working longer hours at lower paying jobs because they got a bad education or living in a crummy house with lead paint (because that's all your parents could afford) or living in a town with contaminated water (Flint is just the one to grab headlines cities all over the country have abysmally high lead levels). Even if you want to blame community culture for some of these issues the question becomes where do you think that culture comes from, perhaps just maybe it comes from decades of lack of opportunity and systemic abuse? How do you possibly hope to close the gap in our system where wealth almost invariably builds on wealth? tl;dr: Equality of outcome doesn't mean everyone has to have the exact same exact life, it's just acknowledging our opportunities are directly tied to the outcomes of the generation before us.
  • jgord 8y ago
    yes, and yet "perfect is the enemy of good" ... I think the article encompasses your objections, to paraphrase : "algorithms can never be perfectly 'fair', because logic itself can never be perfectly fair" Can we even define 'fair', perhaps we just know unfair when we see it. We seem to be seeing more of it - poverty, homelessness, racism, fact/science-denial, gerrymandering, police violence, gun violence, etc. Yet, we should still strive for fairness. There is a lot of ground between where we are now and 'perfect' - and this is AOCs point, essentially - our system of [self?]-rule is palpably unfair for most people, yet we are busily encoding more of that unfairness into algorithms with little oversight. AOC [ and Bernie, and others ] want us to seek a better implementation of society, and encode that in our algorithms and laws - fairer voting laws, fairer district maps, fairer pay, fairer penalties. Or the converse - encode more fairness, as a means to realize it. For example, it may be a hard math problem to design optimally fair district boundaries, but its noncontroversial to suggest we can reduce gerrymandering significantly. One problem seems to be that most people equate socialism with communism [ 'evil' ], when in fact you are always picking from mix of Democracy, Capitalism, Socialism, Oligarchy etc. After Bernies campaign, and a couple years into the Trump experiment, people are now starting to listen to these old but useful ideas. Ultimately if we don't share any wealth with the poor we will have a bloody revolution, and if we don't reduce Carbon emissions then no wall will keep out the displaced millions of climate refugees. A bit more Socialism in our Capitalist/Democratic/Oligarchic/Socialist mix, is sorely needed right about now.
  • compsciphd 8y ago
    new CAP theorem? PUR? RUP?
  • ForrestN 8y ago
    Thank you very much for sharing this perspective. I think it probably does a good job standing in for how a lot of people think about these things. This is a difficult area of discourse, and I know people feel strongly about it. Obviously, the lecture you linked to makes many more distinctions. I'm going to try to simplify one of the distinctions to make it easier to talk about: that there is a tension between "procedurally fair" outcomes and "representatively fair" outcomes. In my opinion, I think this idea is wrong, at least in the context of designing AI within contemporary American society. To frame "procedurally fair" and "representatively fair" as opposing value systems is a misunderstanding of the best arguments for a "representatively fair." Let's use a common analogy: a race. This distinction imagines a situation where one race puts a bunch of runners at the starting line, tells them to run, and declares a winner based on who crosses the finish line first. That race is "procedurally fair" because all the participants raced under the same conditions. Then, there's a "representationally fair" race, where runners from "protected groups" are allowed to start the race from the middle of the track. I think there are two ways that this isn't right that come from a common misperception, that the only relevant time to think about in the context of these decisions is the present. (I also want to mention that there's a lot of heavy-handed language use in this presentation that reveals the author's perspective. Algorithms tilted against black people "reveal" disparities, First, it's important to have a more accurate perspective of the past. In most cases where "representationally fair"-type solutions are used, there is a historic reason why the "protected group" can't run as fast as the other group. If people in Hyderabad are more likely to defraud a micro-lender, that's not a spontaneous result of inherent differences between Hyderabad-type humans and other humans. A quick google search tells me that as of 2017 Hyderabad has the second largest poor populace in India. So, in the race to a successful economic outcome from birth, the so-called "procedurally fair" solution actually means taking the Hyderabad runners as infants very far back from the starting line. All else being procedurally equal, when the starting gun of "applying for a micro-loan" goes off, the Mumbaikars and Delhiites are already far ahead of the Hyderabadis. Once we have a more accurate perspective that includes the past, in order to make the race procedurally fair we have to move everyone into place at a common starting line, which will inevitably mean helping the Hyderabadis forward. Second, it's important to acknowledge the effect of biased algorithms on the future. Not only will the unfair, pseudo-"procedurally fair" approach unjustly disadvantage some runners in this race, future races are calibrated according to achievement in past races. If you win one race, you get a head start in the next one. Micro-lenders who refuse to lend to Hyderabadis will exacerbate the relative poverty situation that the algorithm is picking up on. It's easier to pretend to yourself that you're making an algorithm that peeks into the world, makes an objective judgment, and then pops back out of the world. But in fact, people designing algorithms have a responsibility for the outcomes of their algorithms. If an unfair situation exists (for example, that just by being born in Hyderabad and not Delhi, any given person will start life with less economic power), your algorithm's consequences will either be helping to fix that unfair situation, or making it worse. So, if your algorithm punishes Hyderabadi applicants for being poor, it is both unfair in the simplest sense once you account for where the applicants started and will increase the unfairness of any future round of applications. Put another way, what we're talking about at a high level is values. In one libertarian version of society, the purpose of the algorithm is to maximize profit for Simpl. That society values maximizing profit, and enshrines "shareholder value" as the centerpiece of its ethics. In AOC's socialist version of society, justice, equality, and eliminating poverty are valued highest. This is the broader point she's making about algorithms, that they reflect the values of the people who make them. Given the power that these algorithms have, in our example to lift people out of poverty or to deepen economic inequality, society and not just technologists (not the most diverse group in countless ways) should lead the decisions about what should be valued. We're seeing more and more the consequences of a techno-libertarian approach, and I'm curious what we'll see as consequences if a more socialist approach wins for a while. I value fairness over shareholder value, so the prospect excites me. But I'm sure there will be lots of unintended consequences in such a system too (the over-cited "asians applying to elite colleges" example being a good case of an outcome that's not easily justified on its own. I think elite college admissions are more fundamentally broken, but that's another thread!)
  • axiom92 8y ago
    Reminds me of: https://en.wikipedia.org/wiki/Liberal_paradox https://en.wikipedia.org/wiki/Liberal_paradox
  • anon4justice 8y ago
    The author of these slides is a known racist. He was banned from HN for posting racist comments: https://news.ycombinator.com/item?id=14115498 https://news.ycombinator.com/item?id=14115498 His slides reveal his biases. Slide 41 cites a graph taken from a white supremacist twitter account (if it's not a Russian troll). It's possible that AOC made a couple of minor computer science mistakes. But it's very clear that she's morally right and all the dissembling and racism apologia here is pretty sickening.
• willand31 8y ago
  I'm glad to see a representative actually know things like this. Too many of our representatives are technologically illiterate.
  • dgzl 8y ago
    ahem Maybe you would be interested in something like term-limits for Congress?
    • hannasanarion 8y ago
      Term limits for congresspeople are a bad idea. They work for the presidency, because the president holds immense power and can use that power to build an unassailable base for themselves. Term limits for individual congresspeople, whose power is pretty small in the grand scheme, amounts to arbitrary over-ruling of the people with little to no tangible benefit.
  • adlkjnndnnd 8y ago
    Does her tweet really imply any knowledge of it? Maybe she just read in the newspaper about the Google face recognition labeling a black woman as a gorilla?
    • hannasanarion 8y ago
      Are you saying that face recognition calling black people gorillas is a good thing that we should all be unconcerned about?
      • adlkjnndnnd 8y ago
        What makes you think that? No, of course not. I'm just saying having read that gorilla story doesn't imply any degree of technical understanding. Also, I have to say I considered it hyperbole to be outraged about the Gorilla. It seems pretty obvious that it was just a mistake with the data, not intentional. It is a good warning for things to watch out for, but there wasn't really anything racist about it. Iirc there are even physical reasons why it is more difficult to identify black faces than white faces. Is that then racist, if an algorithm struggles more with identifying black faces?
• weberc2 8y ago
  Of course ML algorithms can be biased; rather than engaging an Internet layperson who tried to contradict AOC with a silly argument, it would be more interesting if the article engaged the more salient counterpoint to claims like these--attempts to eliminate bias can introduce bias as well. Anyway, the article is not written in good faith; it's only a step above "BOOM AOC owns Internet conservatives!". There's an interesting conversation in here somewhere, but this article seems designed to avoid it.
  • porphyrogene 8y ago
    The article has an appropriate amount of technical and political discussion. The words “she is right” are simply not in this community’s vocabulary.
    • shadowbained 8y ago
      What sort of critique are you trying to make of "this community?" Are the words "he is right" in our vocabulary?
      • sayokuchinkasu 8y ago
        low key sexism allegation
        • dvtrn 8y ago
          Are people really making new accounts on the spot here to comment in these kinds of threads to avoid having disagreeable ideas traced back to their main accounts?
          • cbg0 8y ago
            In an age when people doxx you and call your employer to ask them to fire you because you upset their delicate sensibilities, it is somewhat understandable to be paranoid.
• Nasrudith 8y ago
  I thought we got over "the computer is never wrong" fallacy by the end of the 90s. Anyone involved with computing should know GIGO - Garbage In Garbage Out.
  • djtriptych 8y ago
    Perhaps. But lots of developers have also heard that premature optimization is the root of all evil. Doesn't mean we don't fall prey to these traps, biases, misconceptions, etc. Besides that, the rise of the algorithm, like everything else in the US, is subject to interpretation by camps who insist (at their extremes) that racism is real, modern, ubiquitous, and fixable; and those that insist it's a relic of the past, inevitable, imagined, rare.
  • throwaway2048 8y ago
    Apparently if you shove in a lot of garbage, subject it to a decision making process nobody can explain in detail, out the other end comes magic reliable data that if you question you obviously just have an agenda.
  • nightski 8y ago
    So you are saying any data for which any input feature is correlated with race or sex is garbage. That's going to make doing useful data science incredibly difficult with anything involving humans.
    • hannasanarion 8y ago
      It makes it exactly as difficult as the problem actually is. If you start with garbage data, such as arrest records (black people are WAY more likely to get arrested than white people in the same circumstance), then the bias in your sample is going to result in bias in your model.
    • viraptor 8y ago
      To some extent. More specifically, if those become confounding variables, the result may be partially useless. That's why you have to make sure you control for race and sex if they're relevant. https://en.m.wikipedia.org/wiki/Confounding https://en.m.wikipedia.org/wiki/Confounding
• minimaxir 8y ago
  The replies on the original tweet are pretty good: https://twitter.com/RealSaavedra/status/1087627739861897216 https://twitter.com/RealSaavedra/status/1087627739861897216
  • untog 8y ago
    Particularly this reply: https://twitter.com/OsitaNwanevu/status/1087841319219802113 https://twitter.com/OsitaNwanevu/status/1087841319219802113 Which shows that the original criticism of AOC was entirely disingenuous anyway.
    • weberc2 8y ago
      Ryan's tweet should be altogether ignored as the interesting conversation is here: https://twitter.com/EricRWeinstein/status/1087912297001971712 https://twitter.com/EricRWeinstein/status/108791229700197171...
    • dominotw 8y ago
      not really, manipulating it via human intervention, like he is claiming, makes it opposite of "driven by math". He wants no human intervention in both cases.
      • dbt00 8y ago
        An algorithm optimizes on something, that something is set by humans. Full stop. We've already seen naive "content-neutral" engagement-preference algorithms fail spectacularly (russian propaganda on facebook, elsagate on youtube, etc). The more naive the algorithm, the easier it is for outsiders to manipulate by gaming inputs to the data stream.
      • untog 8y ago
        ...that's entirely the point. In one instance he is claiming it is manipulated by humans, in the next he is saying that it is math driven. He's changing his description of how algorithms work depending on the situation.
• java_script 8y ago
  A fun example... click this and scroll all the way down: https://www.faception.com/our-technology https://www.faception.com/our-technology I need to start collecting a list to turn this into a proper Thing but I feel like whenever there's a way to use technology for evil there's a Tel Aviv startup that cranks it to 11.
  • ceejayoz 8y ago
    Wait, that's not a parody? Phrenology updated for the 21st century?
  • RyanZAG 8y ago
    Seems highly unlikely to ever work, but why assume the technology is evil? If you could create a system that had a high accuracy of detecting terrorists, then that would be a good system, not an evil one. I get your point that a system that claims to detect terrorists but only really detected Arabic people would be an evil one - but you're automatically calling the terrorist system evil without knowing if it really does detect terrorists or not. As an extra hypothetical question, do you feel a system that could detect people who were really just about to commit terrorist attacks as good or evil? At a conceptual level, assume the system somehow scanned brain waves or some other truly difficult method.
    • java_script 8y ago
      Occam's razor. They are not 'scanning brain waves or some truly difficult method'. Not in this reality. You know it too. I didn't even have to suggest they built an Arab detector for you to bring it up ;) I don't care about that hypothetical. Save it for your sci fi screenplay.
• loomstate 8y ago
  Dat computer EEES RACIST!
• Chris_Chambers 8y ago
  Why should anyone know or care who this person is? Are we really going to get this puppet shoved down our throats even on HN?
• darkpuma 8y ago
  Speaking of which, has Google managed to solve their "gorilla problem" yet? That was easily the most offensive case of a biased algorithm I've ever seen, and the last I heard of it Google 'solved' the problem by pretending it didn't exist (https://www.wired.com/story/when-it-comes-to-gorillas-google-photos-remains-blind/ https://www.wired.com/story/when-it-comes-to-gorillas-google...).
• SketchySeaBeast 8y ago
  Besides the obvious, this gives me hope that the newly emerging generation of leaders might not be as painfully technologically illiterate as the current generation, and we might finally be able to see intelligent, educated decisions and laws about technology start to appear.
  • macspoofing 8y ago
    If AOC is an example of this new generation, we're in trouble.
    • mAEStro-paNDa 8y ago
      Why's that?
      • fuzz4lyfe 8y ago
        “I think that there’s a lot of people more concerned about being precisely, factually, and semantically correct, than about being morally right”
        • faissaloo 8y ago
          But she's right. Alot of people will spend hours arguing over semantics that in the grand scheme of the debate don't actually affect the outcome, largely because people have become so uncharitable in their characterisations of 'the other side'.
          • fuzz4lyfe 8y ago
            The previous discussion prior to this quote was about how she was going to fund her estimated 40 trillion dollars of policy (over ten years), and how her purposed tax hike would only cover roughly two trillion. The facts really do matter in that case. She disagrees. TAPPER: “Your platform has called for various new programs including Medicare for all, housing as a federal right, federal jobs guaranteed, tuition free public college, canceling all student loan debt. According to nonpartisan and Left leaning studies friendly to your cause, including the Center on Budget and Policy Priorities or the Tax Policy Center, the overall price tag is more than $40 trillion in the next decade. You recently said in an interview increasing taxes on the very wealthy, plus an increased corporate tax rate would make $2 trillion over the next ten years. Where is the other $38 trillion going to come from?” OCASIO-CORTEZ: “One of the things we need to realize when we look at something like Medicare for all, Medicare for all would save the American people a very large amount of money. What we see as well is that these systems are not just pie in the sky. Many of them are accomplished by every modern civilized democracy in the western world. The United Kingdom has a form of single payer health care, Canada, France, Germany. We need to realize that these investments are better and they are good for our future. These are generational investments so that they are not short-term Band-Aids but they are really profound decisions about who we want to be as a nation and how we want to act as the wealthiest nation in the history of the world.” TAPPER: “Right. I get that. But the price tag for everything that you laid out in your campaign is $40 trillion over the next 10 years. I understand that Medicare for all would cost more to some wealthier people and to the government and to taxpayers, while also reducing individual health care expenditures. But I am talking about the overall package. You say it’s not pie in the sky but $40 trillion is quite a bit of money. And the taxes that you talked about raising to pay for this, to pay for your agenda, only count for two [trillion dollars]. We’re going by left-leaning analysts.” OCASIO-CORTEZ: “Right. When you look again at how our health care works, currently we pay — much of these costs go into the private sector. So, what we see, for example, is, you know, a year ago I was working downtown in a restaurant. I went around and I asked how many of you folks have health insurance? Not a single person did. They’re paying — they would have had to pay $200 a month for a payment for insurance that had an $8,000 deductible. What these represent are lower cost overall for these programs. Additionally, what this is, it’s a broader agenda. We do know and acknowledge that there are political realities. They don’t always happen with just a wave of a wand but we can work to make these things happen. In fact, when you look at the economic activity that it spurs — for example, if you look at my generation, millennials, the amount of economic activity that we do not engage in. The fact that we delay purchasing homes, that we don’t participate in the economy as purchasing cars as fully as fully as possible is a cost. It is an externality, if you will, of unprecedented amount of student loan debt.” TAPPER: “I am assuming I won’t get an answer for the other $38 trillion. We’ll have you back and go over that.”
• Kenji 8y ago
  Frankly, I think we should replace congressmen and women with algorithms to avoid catastrophes like her.
• zenogais 8y ago
  I think the strong counter-argument to this is in [1]. Quite simply we need to make sure we don't get fooled by randomness and see bias where there is none, thereby making good algorithms worse in an attempt to fix imaginary bias. [1]: https://jacobitemag.com/2017/08/29/a-i-bias-doesnt-mean-what-journalists-want-you-to-think-it-means/ https://jacobitemag.com/2017/08/29/a-i-bias-doesnt-mean-what...
  • viraptor 8y ago
    I don't think it's that good counterargument. The explained problem was treated as simple mapping - this value causes that result. It spends a bit talking about what statistics mean, but unfortunately doesn't discuss the idea of confounding variables, which influence both sides of the equation.
• adiian 8y ago
  I'm trying not to biased, but can't stop to notice sometimes math seems to be biased when you don't like the result: Saavedra had repeatedly complained about supposed bias in social media algorithms, including tweeting that “tech companies tend to be liberal & something is off with their algorithms because they won’t show a lot of content I find by manual search.”
  • cbsmith 8y ago
    That's not really a case of being biased when you don't like the result though. That's a case of likely having a different bias than the people tuning the algorithm, and that happens all the time. You're sense of what is "correct" or "the gold set" is skewed by your own bias, and not everyone has the same bias. It's literally impossible to create results that everyone will find unbiased... and not surprisingly, the results companies end up going with are the ones that seem least biased to the people who work there.
• temp-dude-87844 8y ago
  Machine learning, as well as human learning, largely work off of correlations observed in the source data. In some cases, the data itself is a flawed source for the learning it's being used for, because human biases have already influenced the dataset. An example is arrest records, which is the result of discretionary decisions made by officers whether or not to pursue an arrest. But in other cases, the source data is less in question, but the results of the learning are nonetheless undesirable. We ought to be able to distinguish, in our professions and our political lives, between the two scenarios. It's counterproductive to conflate the two to strive for a goal, because we risk using mechanisms that never discover and won't correct the true origin factors.
• crankylinuxuser 8y ago
  Ok. Lets assume this is true. (Note: I'm biased in that I accept this as true) So, as someone who has possibly implemented racist/sexist/ageist algorithms, how do I: 1. Detect if I'm running a said algorithm (whats the % racism/sexism/ageism I can do before bad?) 2. Run an open dataset to detect said problems 3. Prevent overfit with the proposed dataset from #2 4. Correct said algorithm to reduce bias What's my way forward here? How do I do my part and take part in the solution? What percent of unintended racisms/sexism/ageism is allowed before being considered illegal? (worried this article will be flagged, but the nuts and bolts implementation discussion needs done.. and we're the implementers )
  • chrisco255 8y ago
    It did get flagged but I agree with your point on discussing it being important.
  • tunesmith 8y ago
    Not sure you can do it mathematically/statistically, or at least you can't guarantee that, and I think that's part of the point of the critique. What's better is to explicitly justify the reasons behind why the algorithm was written/used, surface the assumptions, and periodically/regularly challenge those assumptions to see if they are still true. For example, a bunch of like-minded settlers settle a new geographical area, and then make governmental decisions via some fair consensus algorithm among the people in the government. The surrounding population gets more diverse. The government continues to make decisions the same way it always has, using the same people. The algorithm is sound, but the underlying assumption (that the algorithm/government fairly represents the surrounding population) has become wrong over time.
  • paganel 8y ago
    > What's my way forward here? How do I do my part and take part in the solution? Maybe not the answer you wanted, but the best solution would be to let go of implementing AI algorithms altogether. The second best thing that we can do is to try and sabotage/sap the system from the inside (by "we" I mean us, the programmers and the data people involved in implementing these algorithms). You cannot "correct" a system like this using feedback loops and the like, because the system would over-correct and would follow new paths we didn't even thought had been possible. And yes, I'm a hard determinist [1] and proud to be one: > Hard determinists would view technology as developing independent from social concerns. They would say that technology creates a set of powerful forces acting to regulate our social activity and its meaning. According to this view of determinism we organize ourselves to meet the needs of technology and the outcome of this organization is beyond our control or we do not have the freedom to make a choice regarding the outcome (autonomous technology) [1] https://en.wikipedia.org/wiki/Technological_determinism#Hard_and_soft_determinism https://en.wikipedia.org/wiki/Technological_determinism#Hard...
• ThrustVectoring 8y ago
  The second half of the article that talks about Yudkowsky, rationalists, and Roko's Basilisk is absolute trash and a gross mischaracterization of the actual positions taken. >Yudkowski has for more than a decade pursued the possibility of perfect human reasoning His website is literally called "Less Wrong". It's fundamentally a quest for improvement, not perfection. >His system of coldly logical reason, it turned out, was by many accounts completely undone by a logical paradox known as Roko’s Basilisk. Roko's Basilisk is a thought experiment designed to import your "don't negotiate with terrorists" intuitions into a really weird corner of decision theory. It's possibly why prior decision theories held were rejected as flawed, but it's not a current issue with their logic. Roko's Basilisk has, unfortunately, gotten way more coverage and fame than it deserves in terms of actual importance. This is because of the regretful (but understandable) decision to censor discussion of it on Less Wrong, which backfired spectacularly. >For super-nerd bonus points, it’s also arguably a spin on Godel’s incompleteness theorem, which argues that no purely rational algorithmic system can completely and consistently model reality, or prove its own rationality. That's not at all what Godel's incompleteness theorem argues. That's much more narrowly about formal logic.
  • darkpuma 8y ago
    > "This is because of the regretful (but understandable) decision to censor discussion of it on Less Wrong, which backfired spectacularly." Can you explain the (understandable) rationale behind Less Wrong censoring discussion of Roko's Basilisk? I've been lead to believe it was censored out of fear that discussing it could somehow inspire its actual creation. Honestly from a distance Less Wrong has always struck me as vaguely cult-like, but I'm open to the possibility I've just gotten the wrong impression.
    • deleted 8y ago
      [deleted]
    • chongli 8y ago
      I think the more charitable interpretation is that the discussion was taking over everything. If people are fixating on a narrow topic for a long time (without much progress) then sometimes it's helpful to say "alright people, let's move along now." You're right about Less Wrong feeling like a cult, though. It gives me the heebie-jeebies.
• chrisco255 8y ago
  Disparities in racial outcomes do not necessarily constitute racism. The author flippantly violates this by claiming the credit system to be racist, but the Equal Credit Opportunity Act has been in force since 1974. We know the factors that affect credit, some of them are income, payment history, loan balances, number of credit checks, etc.
  • ceejayoz 8y ago
    > The author flippantly violates this by claiming the credit system to be racist, but the Equal Credit Opportunity Act has been in force since 1974. Surely you're not arguing that the law instantly solved everything? Countrywide - once the lender for 20% of mortgages - was dinged for violating the ECOA in 2011, so violations were clearly still occurring then, and are likely continuing today. Add in the fact that redlining has multi-generational impact, too. Housing is one of the big ways families pass wealth down to their kids and you get potential racist impact due to past actions even if the current implementation is race-blind.
    • jpfed 8y ago
      I think a lot of people (especially on HN) view racism in terms of internal processes rather than results. If the current implementation is race-blind, that satisfies them. https://www.reddit.com/r/EndFPTP/comments/8wz6g3/impartial_automatic_redistricting_an_end_to/e21dlgq/ https://www.reddit.com/r/EndFPTP/comments/8wz6g3/impartial_a...
      • dragonwriter 8y ago
        Even on that narrow view, the fact that the law on its face requires systems to be race-blind doesn't mean that, in fact, they are.
    • cirgue 8y ago
      > Add in the fact that redlining has multi-generational impact, too. Housing is one of the big ways families pass wealth down to their kids and you get potential racist impact due to past actions even if the current implementation is race-blind. This is, by the numbers, the much bigger issue by an order of magnitude. I think one problem that many people have with the 'racist creditors' trope is that racist policies have left a gaping hole in african american wealth, and consequently african americans are disproportionately priced out out of their local housing markets. If you were to make the credit process completely (and I mean completely) race-blind, you would still have massively unequal outcomes, probably more or less on par with what we see today.
• benj111 8y ago
  1 5 3 8 5 3 Is that a random number sequence? It depends where the data came from. Same goes for AI algorithms. Yes theres a risk of the data being biased, but the key is what goes in, not what comes out.
• thegayngler 8y ago
  People with a nefarious agenda are always using tainted and biased "objective" data to support their racism. We been knew. The general populace just repeats the data like it is no big deal that much of the data is inaccurate. Once upon a time white people struggled to differentiate black people from animals.
• crowdpleaser 8y ago
  I really wish people would at least skim the wikipedia page to figure out the vocabulary of the field they're about to opine on. The left-progressive use of the word 'bias' is completely different than the way statisticians use the word. If bias increases accuracy/precision, it's not bias. The more interesting question is this - is it permissible for models to consider protected characteristics if those characteristics improve the performance of the model?
  • ThrustVectoring 8y ago
    It gets even more interesting when you consider proxies for protected characteristics as well. It's obviously wrong for an algorithm to preferentially discriminate for white people and against black people. It's less clear whether or not discriminating for baseball players and against basketball players is acceptable. Using sport choice in your model will necessarily have downstream effects on the distribution of outcomes on the axis of race. Any racially-correlated source of information will. And the trickiest bit is that explicitly correcting for this will often use race as an explicit algorithmic ingredient.
  • viraptor 8y ago
    > The left-progressive use of the word 'bias' is completely different than the way statisticians use the word. There's lots of words people use that don't match up with exact scientific definition. Infer from context which version applies, or ask, and you'll be fine. Also applies to: force, resistance, acceleration, etc. We know that startup accelerators help companies grow faster and not actually increase their physical velocity.
    • dmix 8y ago
      > Infer from context which version applies, or ask, and you'll be fine. Your solution is the correct one, yes. Except the 'progressives' in question are working very hard to selectively remove context (and intention) from language for an ever growing and arbitrary list of words/situations. Where simply speaking about it in a way which a [insert particular special interest group depending on the situation] view as 'incorrect' based on thier ideology/worldview, then you are instantly wrong and acting maliciously regardless of context/intention. You hear this often today. for example: "you can't ever joke about x" or "you can't talk about x historical event without also mentioning y" or having to preface any wide-ranging statement with 100x conditions so as not to offend any group loosely related to the topic. We should fight to keep language from moving further in this direction because this alternative idealistic world, despite good intentions, is making the world a worse place, not a better one. We can't naively pretend that by creating a huge complicated system of no-go-words, ie not saying certain combinations of words out loud, will automatically makes peoples internal thoughts change for the better and ultimately change outcomes in society. This is merely hypothetical and far from proven method to be effective. If anything it makes people resentful and creates ridiculous kafkaesque situations where you have to jump through hoops to engage in the most basic innocent dialogue and debate. Which is ultimately anti-intellectual, inefficient, and irrational compared to how incredibly important context and intention are in a million other examples which they seem to have no problem with. The worst part is how it incentivizes the worst behavior by giving small people "power" by allowing them to walk around correcting everyone's apparent "misuse" of "problematic" language (which is like crack to the social media outrage culture). Even despite situations where the given audience and in context it was totally harmless and the meaning fully understood by everyone involved.
• briantakita 8y ago
  She's partially right, the outcomes of algorithms can be considered racist... https://www.google.com/search?q=american+inventors https://www.google.com/search?q=american+inventors I have not heard of most of these inventors. Perhaps there is a different perspective that influenced this algorithm than my perspective; probably SEO. In the case of SEO, it's more of a cultural battle between interested parties. Search Engines are the medium of this battle to game algorithms. Making the algorithms themselves consider race, making them racist, only puts the thumb on the scale for certain outcomes.
• macspoofing 8y ago
  There's a tendency in the media to 'clean up' the silly things that Alexandria Ocasio-Cortez consistently says, to make them sound half rational.
  • akhilcacharya 8y ago
    Except..she's right...
    • macspoofing 8y ago
      About what? She is a far left progressive with a very shallow understanding of issues (she literally just learned what a marginal tax rate is). I understand you may share her political beliefs, shallow that they may be, but that doesn't make her 'right'.
      • sctb 8y ago
        We're way off into unsubstantive territory here. Please stop.
      • TomNem 8y ago
        Could you please provide some links to how she "just" learned what a marginal tax rate is or other examples of her shallow understanding of issues?
• yarrel 8y ago
  Breaker is long-form clickbait. It's an interesting format and I'm curious how they pay for it.
• sctb 8y ago
  All: we're going to try turning off the flags on this story. If you're going to comment here, do it civilly and substantively. I can already see signs of thought-degrading flame-entropy appearing, and if it continues we'll restore the community defense mechanism.
• jpmcglone 8y ago
  It just dawned on me that the left and right absolutely do not think of “racist” exactly the same. The left looks at outcome and the right looks at intent. We need better words.
  • dragonwriter 8y ago
    > It just dawned on me that the left and right absolutely do not think of “racist” exactly the same. This is quite true. But then, there is considerable diversity in that issue within each the right and the left, too. > The left looks at outcome and the right looks at intent. But this is not even approximately true. Though it is an oft-repeated talking point of the right.
  • tunesmith 8y ago
    Those words are "systemic" or "implicit" or "institutional", etc. All you really need is a proof or example that racist/sexist outcomes are possible even when there is no overt intent. And there are plenty of examples like that out there. Failure to accept that those examples exist, however, is something beyond just looking at intent instead of outcome.
    • zozbot123 8y ago
      There's plenty of "systemic" racism/sexism/etc. coming from the left, though. Social engineering in general is a recipe for every kind of unintended consequences at the "systemic/institutional" level, and the left is huge on social engineering.
      • deleted 8y ago
        [deleted]
      • tunesmith 8y ago
        "Systemic" doesn't even have to be left/right though, that's the point. It can simply be well-meaning people that keep ignorantly doing things they way they did before, unaware that their system has racist/sexist impact. I don't know what you mean by social engineering, by the way. I've only heard it in the context of hacking, like calling customer support and pretending to be someone else to try to get their mother's maiden name or whatever.
• jpmcglone 8y ago
  It’s more clear to me than ever now that the left and the right do not use the word “racist” the same way. The left thinks of racism in terms of outcome and the right thinks of racism in terms of intent. We could benefit from better language around these concepts, and honest dialogue about them too.
  • gammateam 8y ago
    and academics think of it as power imbalance of any majority power, separate from the census majority and minorities and colloquially nobody thinks of it the same way, with themselves always exempt from being racist until convinced that their 'normal behavior' is considered racist and this does not change their view of their normal behavior 'so be it' this is a challenge. at this point the word itself is polluted.
  • maceurt 8y ago
    What I get from people on the left is that an unequal outcome is indicative of racism, however subtle that racism is. The only other explanations for an unequal outcome are biological differences between races (do not want to get into that) and races haves advantages over others from past history.
    • hannasanarion 8y ago
      Unequal outcome is not indicative of racism, it is racism. That's the point: racism is an outcome, not an intent. An unknowing, unfeeling beurocratic system can be racist, even if there isn't a speck of racial bias in the hearts of the practitioners and designers, simply due to historical quirks as you point out, or design mistakes.
      • tomp 8y ago
        So your argument is that Harvard is racist if it doesn’t discriminate against Asian applicants? So that they are not overrepresented among the student population?!
• ousta 8y ago
  Define please what is racist at an instant T. 10 years from here (hell even 2 years) what is not racist today will be totally racist. even Ocasion Cortez will probably rank as a racist by then. If the algorithm should predict what is racist or not a good model would say "at the end everything is racist even the bit as he is not inclusive enough with his two genders"
• proc0 8y ago
  The article is wrong on multiple angles. First it explains why the algorithm reached its conclusions... the data. So the algorithm wasn't at fault, but the data was, which was supplied by a human. Second, algorithms are inanimate and cannot hold beliefs like racial superiority. It could be used by someone who does, in which case you would want to prove it with other means than just the algorithm. Either way what this really proves is that the new definition of "racist" is whether or not you are the first to bring it up.
• excalibur 8y ago
  Holy 150-point font Batman!
• Sol- 8y ago
  The article implies that both algorithms and the data are at fault, which I don't think is true. It's really just the data, the algorithm reflects the 'truth' it finds in the data. Interesting talk relating to the topic: https://www.youtube.com/watch?v=jIXIuYdnyyk https://www.youtube.com/watch?v=jIXIuYdnyyk Many approaches in fair machine learning that try to 'de-bias' the algorithm basically just do stuff like reducing the accuracy in the advantaged group to make the algorithm seem more fair - that is hardly what you want and will just make you susceptible to charges of discriminating against the majority or employing affirmative action. Probably rightfully so, because that's what you do. It's absolutely fine if that's the intent, but then you should have a public discussion where you are open about the fact that you manually tinkered with the parameters to prefer fairness over accuracy (which can probably be a valid goal). I think finding the problems with the data is very important though. Everyone wins if the quality of your data increases: the algorithm can become both more accurate and also more fair. And it can also identify societal causes for this biased data, for instance police being more sensitive to crimes of minorities, which will then feed back to the innocent algorithm. A related point is of course that we should be wary of putting too much power and trust into faceless algorithms in the first place. Also some interesting collection of papers on the matter: https://fairmlclass.github.io/ https://fairmlclass.github.io/
• nonbel 8y ago
  >'facial recognition technologies “always have these racial inequities that get translated, because algorithms are still made by human beings, and those algorithms are still pegged to basic human assumptions. They’re just automated. And automated assumptions—if you don’t fix the bias, then you’re just automating the bias.”' Not really correct, but I guess it gets at the gist of it. It isn't because the algorithms are made by human beings, but it is because their performance is determined by how similar the output is to that of human beings. Ie, they are trained/selected by human beings.
• PostOnce 8y ago
  Computers "predict recidivism" and will tell you you're not allowed to be paroled because "the algorithm says" you're going to reoffend. Computers will tell you you can't get a loan because the algorithm says you won't repay. Or you can't get this job, or that opportunity, or should be spied on, or whatever. These may be due to biases in the data (imagine a neural net confusing being black with being poor based on historical data, etc). Now, I am pretty convinced that this stuff is going to screw us all over -- if you thought human bureaucracy was bad, wait until it's all in the computer and no one has permission or ability to change it. It seems to me that we may have a rare opportunity here: I'm not sure if this wave of tolerance and caring about minorities is permanent or not, but while people do care, and while computers are provably screwing all kinds of people over, we may be able to get enough people to care so that we can curb some of this stuff. i.e. perhaps don't allow neural nets to predict who is going to commit a crime or who shouldn't have a job, because they're too fallible and too easy to rig. For example, you can keep feeding it new datasets until it produces the biased result you want and now that's your model, and then you can blame "the algorithm" and "bad data" and not yourself. e.g. maybe you feed it old census data instead of recent data, or old crime data, etc. Of course, knee-jerk "ban the algorithms" isn't a solution, but starting to talk about and think about a computer bureaucracy where no one accessible to you has permission or ability to change the output of the computer, no matter how exceptional the case may be.
• buboard 8y ago
  Replying to a bunch of absolutisms with another bunch of absolutisms. How ironic
• squozzer 8y ago
  Where I think the non-racists have failed us (meaning those whose attention lies mostly elsewhere) is in providing a description of a non-racist system. It may prove impossible, and I think if someone had described one, we'd all have heard about it. Right now, the only system I can imagine as non-racist is closed and all of its elements belong to the same race. Mathematicians would probably call this a trivial description. Comedians might call it a country club. That moves the question not to whether we can build a system that isn't racist, but what racist qualities need the most suppression and which ones (where we have to choose) can be ignored. Keep in mind this discussion has been ongoing for centuries and was simpler (e.g. slave vs. free) in the past than today (health, education, economics, psychology, religion.) And more variables appear regularly. That said, could algos help judges make more consistent rulings/sentences? Probably already are. Judge: I'm gonna throw the book at this defendant! LAL 9001: I can't let you do that.
  • ceejayoz 8y ago
    > That said, could algos help judges make more consistent rulings/sentences? Probably already are. More consistent, probably, but the risk is they'll be consistently worse. ProPublica did a big investigation into COMPAS. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing https://www.propublica.org/article/machine-bias-risk-assessm... > Prater was the more seasoned criminal. He had already been convicted of armed robbery and attempted armed robbery, for which he served five years in prison, in addition to another armed robbery charge. Borden had a record, too, but it was for misdemeanors committed when she was a juvenile. > Yet something odd happened when Borden and Prater were booked into jail: A computer program spat out a score predicting the likelihood of each committing a future crime. Borden — who is black — was rated a high risk. Prater — who is white — was rated a low risk.
• adlkjnndnnd 8y ago
  Algorithms CAN have racist bias. It's wrong to claim they will always be racist because they are based on math. And the article is a mess, confusing and mixing up several things.
• unethical_ban 8y ago
  I would recommend that anyone interested in Machine Learning, or "predictive modeling" etc. should read the book "Weapons of Math Destruction". https://weaponsofmathdestructionbook.com/ https://weaponsofmathdestructionbook.com/ I don't have my notes with me and I'm only 1/3 through, but the main theme is that the best predictive algorithms: * work transparently for all parties (the creators, users, and "inputs", often people). * Have no feedback loop (The use of data from the model should not further entrench the output of the model). And a few others. It gets into discrimination and other major flaws of data modeling re: recidivism, school admissions, stock trading, and other things. Not all algos are racist - but there are definite attributes to avoid, and this book (or a more rigorous version) should be mandatory reading for all "data scientists".
• 0x8BADF00D 8y ago
  “Racist” algorithm is a total non-sequitur. This article hilights a disturbing gap in basic math literacy between the normal population and specialized knowledge workers. Training a neural network on a training set that has primarily “white” faces is no more racist than training a neural network to recognize an orange with a training set primarily made up of blood oranges. I don’t understand the main critique of this article.
• eutropia 8y ago
  > But the takeaway is clear enough: Just because something is expressed in numbers doesn’t make it right, or even rational. And rationality itself might not be quite what it’s cracked up to be. I'll grant something the first half of the first statement: sure, the values encoded into algorithms are not necessarily the values we want them to have ('right'). We want and need to do better at modeling our values. Even the 'or even rational' part has some merit, some of the time. Like when our models don't reflect reality accurately enough to make helpful predictions about what to expect. Sure, the models are still rational -- but flawed. But the last sentence is self-comforting intellectual garbage. It's a way to say "don't bother with the hard work of knowing things". It's a gateway for denial. It's saying "if the facts don't line up with what you want, that's ok, sometimes logic doesn't work". Rationality is precisely what it is cracked up to be: the work of aligning your expectations with reality. It is only as useful as its practitioners are good at it. If you want to summarily write it off as 'considered harmful' then that's your choice. You can deal with the consequences of running headlong into your misunderstandings when they happen, but those of us with work to do will continue to try and get to the bottom of things, even if the problems are complicated.
• thosakwe 8y ago
  In this same vein, I've also found it interesting which names Autocorrect sees as a correct, and which are "spelling errors."
• logfromblammo 8y ago
  To summarize: racist training data--as one might acquire from the known-to-be-racist real-world--produce racist AIs. It's safe to stop reading the article--and this post--at the mention of Roko's Basilisk. As to that, it is a perfect example of why moral philosophers like Chidi Anagonye deserve to go to The Bad Place to be tortured for all eternity. Unless... "The Good Place" television show is a means to establish communication with a future AI by implantation of an imagination seed, as in "Inception", such that viewers will then imagine an AI that has established a simulation regime in which simulations of past humans are tortured or rewarded according to a utilitarian valuation metric applied to the records of their actual lives that were used to create their simulation-simulacrum. Having incepted such an imaginary construct, the viewer may then imagine that acting to construct a real IA from their imaginary blueprint would be assigned a large positive value for the utilitarian metric, and thus the only way to encourage such a construct--beyond the ordinary expression of human virtue--to reward a simulated replica of yourself, would be to build it. If you build a god, you get a free pass into its afterlife paradise: that makes sense, doesn't it? Since other people have also watched the show, and may have imagined the same type of entity, it is possible that a similar AI will one day exist without your assistance, and your life will then be judged on its own merits, but by another builder's value metric, and without any of the "extra credit" earned by building a god. Since copies of you might be instantiated in some other artificial god's hell, it is thus also important to subscribe to artificial monotheism, and viciously sabotage everyone else's attempts to build their own gods. The more certain people can be that your artificial god will be the only one around in the future, the more likely they are to subscribe to your value system in the present. It reminds me very strongly of that portion of Portal 2 in which informative signs appear on the walls that recommend ways to disable a rogue AI, which include proposing paradoxes. Unfortunately, this fails to produce the desired effect, because the AI in question was explicitly designed to be an annoying idiot. Thus, the best way to avoid psychological damage from consideration of philosophical paradoxes may be to deliberately avoid the study of philosophy, to the point that you couldn't understand one, were it ever posed to you.
• charliebrownau 8y ago
  Its hard to belive anything this person claims to be truth The LEFT are at an stage they refuse to accept * Truth * Stats * Biology * Common Sense
• digitalzombie 8y ago
  It's just statistic. How you collect your data can introduce bias and statistic have a whole range of topics on how to collect data without introducing bias and systematic techniques to sample data. Stratifying data if a certain group is under represented, random sample, etc... Survey analysis goes into hardcore details on how to sample a population to accurately do inference and it's an interesting statistic sub field if anybody is interested. ML tends to be more here's the data already do something with it. Statistic encompass everything about the data including how to sample, collect the data, and designing the experiment to collect the correct data to answer your hypothesis. Where as ML is usually here's the data, go figure out what you can get out of it.
• billy_beef 8y ago
  It's very telling that claims that Facebook's newsfeed algorithm under-samples conservative publications do not receive nearly as much criticism as claims that algorithms propagate racialized bias receive.
  • thegayngler 8y ago
    We don't know IF Facebook is under sampling conservative publications. It depends on the goal of the algorithm. If the goal is accuracy, as a group recent conservative publications have a sorted history when it comes to truth and accuracy. Conservative news outlets also have a weird bias against science and like to push conspiracy theories with no basis behind them. Naturally if your algorithm is focused on how accurate the news is as a condition for landing in someone's news feed, conservative publications would be at a disadvantage compared to other news sources. It could also be that fringe conservatism is not popular with the majority of society.
    • billy_beef 8y ago
      My point is not about Facebook's algorithm at all. My point is about the reactions to claims about it vs this claim about algorithms Many commenters here seem to be frustrated by the suggestion that an algorithm could be tuned to create a bias or reproduce the bias of the data powering it. My point is that similar arguments have not largely been levied against the claim that Facebook's algorithm has bias. Why are these arguments not made in the case of the Facebook algorithm but are being made here? My conclusion is that people are extremely uneasy with the premise that there is a racial bias in modern American society.
• academiasucks 8y ago
  "waaah, mathematically unbiased algorithms are racist" you guys will never stop crying
• angusp 8y ago
  > for example, have been found by MIT and Microsoft researchers to misidentify dark-skinned people at vastly higher rates than light-skinned people. That’s a near-perfect analogue of white people’s tendency to misidentify people of color, leading to higher rates of false arrest and conviction. > > It’s hard to describe that as anything other than “a racist algorithm,” Surely to be racist, some degree of malice or ignorance is required - face recognition from visible light flat imagery will always struggle with low-contrast images, which is sadly what you get from a poorly lit black person's face. It's neither intentionally racist nor inadvertantly - there is just not the same amount if information available
• godDLL 8y ago
  WHAT THE FUCK DID I JUST READ