<story><title>Scientists Increasingly Can’t Explain How AI Works</title><url>https://www.vice.com/en/article/y3pezm/scientists-increasingly-cant-explain-how-ai-works</url></story><parent_chain><item><author>fallingknife</author><text>What people are really saying, but never will admit, is that they want AI to mirror their own biases, not to have none.  That&amp;#x27;s the reason they are &amp;quot;cleaning&amp;quot; the input data.</text></item><item><author>Xcelerate</author><text>There is a somewhat widespread implicit assumption that having sufficient intelligence to develop increasingly sophisticated machine learning implies that we also have sufficient intelligence to develop an “intuitive” understanding of how it works.&lt;p&gt;I think that assumption is totally wrong, and trying to reconcile the two is probably a distraction and a waste of time.&lt;p&gt;Evolution led to human intelligence just fine on its own; why are humans not simply the catalyst in the next stage of this natural process from which something more complex arises?&lt;p&gt;With regard to the moral questions around machine learning, I think people are overthinking things. There needs to be a sharp (societal) line between inferring causation and the capability for prediction. Maximize the latter any way that you can, but a lot of careful thought should be put into how the results of a prediction are utilized. Instead we are currently going about this backward by trying to haphazardly “clean” the input data so that the output gets a free pass on how it can be used. We can never decouple all of the biases and eliminate intra-predictivity in the input data though, so it’s fundamentally a bad approach.</text></item></parent_chain><comment><author>impalallama</author><text>What are you saying?&lt;p&gt;That its better to just blindly trust in the output of a blackbox AI output?
That naively feeding in all possible data into an AI is the &amp;quot;best&amp;quot; way to do it?
It is been well documented that blindly trusting AI just leads to it perpetuating human stereotypes and worsen flawed systems. 
&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;dl.acm.org&amp;#x2F;doi&amp;#x2F;10.1145&amp;#x2F;3531146.3533138&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;dl.acm.org&amp;#x2F;doi&amp;#x2F;10.1145&amp;#x2F;3531146.3533138&lt;/a&gt;&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.aclu.org&amp;#x2F;news&amp;#x2F;privacy-technology&amp;#x2F;algorithms-in-health-care-may-worsen-medical-racism&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.aclu.org&amp;#x2F;news&amp;#x2F;privacy-technology&amp;#x2F;algorithms-in-h...&lt;/a&gt;&lt;p&gt;It&amp;#x27;s not some great insight that offering different inputs will result in different outputs and MAYBE we want to get better output with a different input.</text></comment>
<story><title>Scientists Increasingly Can’t Explain How AI Works</title><url>https://www.vice.com/en/article/y3pezm/scientists-increasingly-cant-explain-how-ai-works</url></story><parent_chain><item><author>fallingknife</author><text>What people are really saying, but never will admit, is that they want AI to mirror their own biases, not to have none.  That&amp;#x27;s the reason they are &amp;quot;cleaning&amp;quot; the input data.</text></item><item><author>Xcelerate</author><text>There is a somewhat widespread implicit assumption that having sufficient intelligence to develop increasingly sophisticated machine learning implies that we also have sufficient intelligence to develop an “intuitive” understanding of how it works.&lt;p&gt;I think that assumption is totally wrong, and trying to reconcile the two is probably a distraction and a waste of time.&lt;p&gt;Evolution led to human intelligence just fine on its own; why are humans not simply the catalyst in the next stage of this natural process from which something more complex arises?&lt;p&gt;With regard to the moral questions around machine learning, I think people are overthinking things. There needs to be a sharp (societal) line between inferring causation and the capability for prediction. Maximize the latter any way that you can, but a lot of careful thought should be put into how the results of a prediction are utilized. Instead we are currently going about this backward by trying to haphazardly “clean” the input data so that the output gets a free pass on how it can be used. We can never decouple all of the biases and eliminate intra-predictivity in the input data though, so it’s fundamentally a bad approach.</text></item></parent_chain><comment><author>diputsmonro</author><text>I completely disagree.  You&amp;#x27;re right that everyone has biases - including the authors of the AI who will therefore choose a biased training dataset. When the tool they are constructing has unparalleled power, and is very likely to negatively impact the lives of those who the authors are biased against (consciously or not), it&amp;#x27;s entirely fair for those impacted groups to be concerned.&lt;p&gt;Every dataset has a skew to it, which should be accounted for during training.  If the authors don&amp;#x27;t explicitly account for such things, out of ignorance or bias, then that skew, that bias, will be ingrained in the AI.&lt;p&gt;For example, if I only train my AI on it&amp;#x27;s knowledge of chocolate from ads for a particular brand, it&amp;#x27;s going to have some very opinionated, very wrong ideas about chocolate.  This example is silly and obvious, but similar skews happen all the time in real datasets that the authors don&amp;#x27;t have the time or expertise to recognize.&lt;p&gt;When people who &lt;i&gt;do&lt;/i&gt; have that expertise speak up, we should listen to them and fix it, not just blindly trust &amp;quot;the data&amp;quot; and whatever our fancy algorithm does with it.  Garbage in, garbage out.</text></comment>