<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>mlyle</author><text>I think there&amp;#x27;s two different issues here.&lt;p&gt;One, if datasets are biased --- if you build your system to only work on white males --- then it may have suboptimal results for other groups.  This is a common problem: you use your company&amp;#x27;s faces, or college students enrolling in data gathering exercises, etc, who are not representative of the population at large.  We can fix this by being careful about dataset bias.&lt;p&gt;But the second issue hits right at the heart of a major societal problem&amp;#x2F;debate.  When we use AI to make decisions about people, will the system become racist--- even with representative datasets?  If you train something to predict, say, odds to default on a loan-- will it figure out things that correlate to race and be making mostly racial decisions?  Different races do have different default rates, but we&amp;#x27;ve decided as a society that it is unfair to use race to determine an individual&amp;#x27;s probability of default.  But if we choose things that are correlates of both default rates and race, when is that fair and when is it just veiled racism (redlining)?  What things are measuring a causal relationship and what things are just racism in disguise?&lt;p&gt;This second problem is much worse with ML, because we have the ability to accept a whole bunch more things into our models and explaining the rationale of why decision are made is much harder.&lt;p&gt;And of course, the first problem-- both bias in datasets during use and biases during research and development -- makes the second problem worse.  It can&amp;#x27;t even necessarily be addressed by broadening the dataset and retraining:  if, in this case, you do your research and training with just white faces, and report positive results, it may not generalize to work as well for everyone with a broader dataset.</text><parent_chain><item><author>andrewflnr</author><text>I think Yann and his critics are focusing on different questions. IIUC Yann is looking at the narrow engineering perspective of how to build an fair ML system. Gebru et al seem to be talking about a much more abstract, societal trend-oriented, &amp;quot;let&amp;#x27;s think about if we should not just if we can&amp;quot; type of question, though it&amp;#x27;s still pretty fuzzy to me. Anyway, the epistemological standards of those questions are basically incompatible (incommensurable?), so discussion is impossible unless they agree on which one they&amp;#x27;re trying to answer. I&amp;#x27;m starting to notice this kind of thing a lot.</text></item><item><author>emtel</author><text>Can anyone provide a simple explanation or example that illustrates why Yann was wrong? I’m highly predisposed to take his side in this, but I wonder if I could be missing something.</text></item></parent_chain></comment><story><title>Yann LeCun quits Twitter amid acrimonious exchanges on AI bias</title><url>https://syncedreview.com/2020/06/30/yann-lecun-quits-twitter-amid-acrimonious-exchanges-on-ai-bias/</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>aaanotherhnfolk</author><text>A variant of this is that both ppl are answering the same question but on different time scales. So one is pro (on a generational timeline) and the other is con (on a 4-year timeline).&lt;p&gt;Both might be right, for their respective time scales, which is why it&amp;#x27;s so hard for either one to back down.&lt;p&gt;Many many ways to talk past one another. We need a small book on techniques to short circuit these situations and salvage conversations.</text><parent_chain><item><author>andrewflnr</author><text>I think Yann and his critics are focusing on different questions. IIUC Yann is looking at the narrow engineering perspective of how to build an fair ML system. Gebru et al seem to be talking about a much more abstract, societal trend-oriented, &amp;quot;let&amp;#x27;s think about if we should not just if we can&amp;quot; type of question, though it&amp;#x27;s still pretty fuzzy to me. Anyway, the epistemological standards of those questions are basically incompatible (incommensurable?), so discussion is impossible unless they agree on which one they&amp;#x27;re trying to answer. I&amp;#x27;m starting to notice this kind of thing a lot.</text></item><item><author>emtel</author><text>Can anyone provide a simple explanation or example that illustrates why Yann was wrong? I’m highly predisposed to take his side in this, but I wonder if I could be missing something.</text></item></parent_chain></comment><story><title>Yann LeCun quits Twitter amid acrimonious exchanges on AI bias</title><url>https://syncedreview.com/2020/06/30/yann-lecun-quits-twitter-amid-acrimonious-exchanges-on-ai-bias/</url></story>