<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>mft_</author><text>It&amp;#x27;s an interesting question, as we have a spectrum from &amp;#x27;little to no transparency&amp;#x27; through to &amp;#x27;full transparency&amp;#x27; (which is pretty rare), and in the middle sits the usual approach of &amp;#x27;communications-team-led messaged quasi-transparency&amp;#x27;.  Difficult to know (without more info) where Shipt would have appeared on this spectrum, but given the issue, they&amp;#x27;re probably somewhere towards the &amp;#x27;insufficient transparency&amp;#x27; end.&lt;p&gt;What&amp;#x27;s silly in this case is that (as others have pointed out) the new algorithm &lt;i&gt;seems&lt;/i&gt; to have been reasonably equitable, with a genuine redistribution of payments, rather than just a cut overall.  Shipt could have avoided this whole situation with a straightforward explanation of the changes, together with a few examples of the cases&amp;#x2F;jobs in which people would earn more or less.</text><parent_chain><item><author>febeling</author><text>I had similar thoughts. But let&amp;#x27;s not overlook the information asymmetry, which contributed to the dissatisfaction. I don&amp;#x27;t want to live in a world which is controlled unilaterally, and intransparently by a group of people who assume they have a full picture of the situation and assume they understand moral completely, and also don&amp;#x27;t think it necessary to explain how they think so highly of themselves.</text></item><item><author>or_am_i</author><text>I am not defending Shipt and there is no doubt gig workers are in a very vulnerable position. However, the data analysis results as presented in the article do not support the article&amp;#x27;s main point. &amp;quot;40% are getting paid at least 10% less&amp;quot; is not unnatural to expect whenever pay is redistributed, especially since some 30+% are getting at least 10% more. Imagine a _hypothetical_ situation where Shipt is 100% on point and driving a fairer version of the algorithm patch removing a way for workers to &amp;quot;optimize&amp;quot; for short, well paid trips, resulting in pay cuts to those who had learnt how to do it, while not changing&amp;#x2F;increasing pay for everyone else. We would see the same kind of result: some portion of workers would get paid 10% less, some 10% more. This does show that workers are paid differently for the same work they have been doing, but does not prove the change is unfair.</text></item></parent_chain></comment><story><title>Shipt’s algorithm squeezed gig workers, who fought back</title><url>https://spectrum.ieee.org/shipt</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>braza</author><text>&amp;gt; I don&amp;#x27;t want to live in a world which is controlled unilaterally, and intransparently by a group of people who assume they have a full picture of the situation and assume they understand moral completely&lt;p&gt;I have thought about this topic for a while at the time that I worked with Law data (e.g. Family Law and Military Law), and I just came to the conclusion that several societal institutions and it&amp;#x27;s agents are inherently intransparent, even in situations where some &amp;quot;illusionist transparency&amp;quot; (there&amp;#x27;s transparency, but the magician deviates your attention to another side) is given (e.g. judiciary system, under-the-table political agreements, etc.).&lt;p&gt;That&amp;#x27;s one of the reasons I would like to have a more algorithmic society with human in the loop calling the final shots and placing the rationale on top. An algorithm will have human and institutional biases but in some sort, you can explain part of it and fine-tune it; a human making the final call on top of a given option would need to explain and rationally explain its decision. At best a human actor will use logic and make the right call, at worst it will transparently expose the biases of the individual.</text><parent_chain><item><author>febeling</author><text>I had similar thoughts. But let&amp;#x27;s not overlook the information asymmetry, which contributed to the dissatisfaction. I don&amp;#x27;t want to live in a world which is controlled unilaterally, and intransparently by a group of people who assume they have a full picture of the situation and assume they understand moral completely, and also don&amp;#x27;t think it necessary to explain how they think so highly of themselves.</text></item><item><author>or_am_i</author><text>I am not defending Shipt and there is no doubt gig workers are in a very vulnerable position. However, the data analysis results as presented in the article do not support the article&amp;#x27;s main point. &amp;quot;40% are getting paid at least 10% less&amp;quot; is not unnatural to expect whenever pay is redistributed, especially since some 30+% are getting at least 10% more. Imagine a _hypothetical_ situation where Shipt is 100% on point and driving a fairer version of the algorithm patch removing a way for workers to &amp;quot;optimize&amp;quot; for short, well paid trips, resulting in pay cuts to those who had learnt how to do it, while not changing&amp;#x2F;increasing pay for everyone else. We would see the same kind of result: some portion of workers would get paid 10% less, some 10% more. This does show that workers are paid differently for the same work they have been doing, but does not prove the change is unfair.</text></item></parent_chain></comment><story><title>Shipt’s algorithm squeezed gig workers, who fought back</title><url>https://spectrum.ieee.org/shipt</url></story>