<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><parent_chain><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><author>Kapura</author><text>This is fundamentally a forest&amp;#x2F;trees type of situation. LeCun sees the issue with this one model, says, &amp;quot;If this had been trained on a different sample it wouldn&amp;#x27;t have this issue,&amp;quot; and stops his train of reasoning there. The problem he is seeing is the mis-trained model, and nothing more.&lt;p&gt;But the problem is larger than this single model, because this issue (or similar ones) are pervasive in the fields in which AI are being employed. If a neural net is helping a court hand down sentences, it is going to be trained on historical sentencing data, and will in turn reflect the biases present in that data. If you are still only seeing the one tree, you say &amp;quot;well we must correct for the historical bias,&amp;quot; and absolve yourself of thinking of the larger problem. That forest problem is that we will always be feeding these algorithms biased inputs, unless we do the work to understand social biases and attempt to rectify them.</text></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><parent_chain><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><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></comment>