<story><title>Car insurers accused of discriminating against people with Hotmail accounts</title><url>https://www.theguardian.com/technology/shortcuts/2018/jan/23/a-sign-that-youre-not-keeping-up-the-trouble-with-hotmail-in-2018</url></story><parent_chain><item><author>razorunreal</author><text>If you can explain why it is OK for car insurance to charge differently by gender, but by race is not OK, I will be impressed.</text></item><item><author>discoursism</author><text>I&amp;#x27;d say there&amp;#x27;s a one really important difference between being Black and using Hotmail, and that difference is the same reason why one is a protected class and one isn&amp;#x27;t.&lt;p&gt;The real concern here isn&amp;#x27;t discriminating against people with Hotmail accounts. On the surface level, there&amp;#x27;s nothing wrong with that. This does become an issue if the property of having a Hotmail account is used as an avenue to discriminate against people based on their membership in a protected class -- a sort of actuarial parallel construction, if you will. At that point, we do have a bit of a problem.&lt;p&gt;&amp;gt; We need regulation in place to stop any punitive decision making, public and private, which can be found in court to be based on correlation instead of causation.&lt;p&gt;So, young men could not be charged more for insurance than young women? In fact, young people in general would have to get the same price as everyone else? People in New York would have to pay the same as someone in rural Alaska? Someone who&amp;#x27;s been driving a year gets charged the same as someone who has been driving fifteen, etc.? Even being in an accident doesn&amp;#x27;t &lt;i&gt;cause&lt;/i&gt; you to get in another accident. It&amp;#x27;s just correlated with that outcome. Throw out the entire actuarial aspect of insurance? These &amp;quot;punitive&amp;quot; charges are all based on correlation. I don&amp;#x27;t see this happening.&lt;p&gt;----&lt;p&gt;That said, as a non-statistician, I&amp;#x27;m interested in what kinds of tests can be run on signals to verify that they&amp;#x27;re not proxies for selecting and discriminating against protected classes. Is there a way to modify a signal using its correlation with membership in these classes such that you can marginalize the attributes you&amp;#x27;re not allowed to discriminate based on?&lt;p&gt;For example, it seems reasonable to believe that hotmail use is correlated with age, race, gender, etc. As an insurance company, I would have information on hand to inform me as to the extent of this correlation, at least among my customer base, and I would in turn know the correlations of those factors with risk. Could I somehow remove the racial, gender, and age components of the Hotmail risk signal to obtain a signal that only conveys the portion of the risk correlation that is not based on those classes? If so, what is that statistical technique called?</text></item><item><author>013a</author><text>Its obvious why this is happening; some form of statistical modeling or AI correlated having a hotmail account with being higher risk.&lt;p&gt;This is a serious problem with all AI that makes decisions like this. Having a Hotmail account is a symptom of a problem which could lead to higher risk, but it is judging that symptom as if it is a cause which leads to risk.&lt;p&gt;Its exactly similar to the supposition that black people statistically commit more crime, and thus should pre-emptively receive harsher bail or be profiled. Unless you can scientifically prove that ethnicity causes crime to happen, its disgusting. This is obvious to us.&lt;p&gt;In year&amp;#x27;s past, correlation fallacies might have meant that black people were profiled by police more. Today, it means that we build AIs which make life-ending decisions like determining repeat offender risk, visa status, employment decisions, all of which predetermines an unknown outcome by correlating your known qualities with the known outcome of people with similar qualities to you.&lt;p&gt;We need regulation in place to stop any punitive decision making, public and private, which can be found in court to be based on correlation instead of causation.</text></item></parent_chain><comment><author>tantalor</author><text>Because they are different things (in the US).&lt;p&gt;Racial discrimination requires &amp;quot;strict scrutiny&amp;quot; to be legal.&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;en.wikipedia.org&amp;#x2F;wiki&amp;#x2F;Strict_scrutiny&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;en.wikipedia.org&amp;#x2F;wiki&amp;#x2F;Strict_scrutiny&lt;/a&gt;&lt;p&gt;Sex discrimination requires &amp;quot;intermediate scrutiny&amp;quot; to be legal.&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;en.wikipedia.org&amp;#x2F;wiki&amp;#x2F;Intermediate_scrutiny&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;en.wikipedia.org&amp;#x2F;wiki&amp;#x2F;Intermediate_scrutiny&lt;/a&gt;&lt;p&gt;This podcast gives a wonderful history:&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.wnyc.org&amp;#x2F;story&amp;#x2F;sex-appeal&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.wnyc.org&amp;#x2F;story&amp;#x2F;sex-appeal&amp;#x2F;&lt;/a&gt;</text></comment>
<story><title>Car insurers accused of discriminating against people with Hotmail accounts</title><url>https://www.theguardian.com/technology/shortcuts/2018/jan/23/a-sign-that-youre-not-keeping-up-the-trouble-with-hotmail-in-2018</url></story><parent_chain><item><author>razorunreal</author><text>If you can explain why it is OK for car insurance to charge differently by gender, but by race is not OK, I will be impressed.</text></item><item><author>discoursism</author><text>I&amp;#x27;d say there&amp;#x27;s a one really important difference between being Black and using Hotmail, and that difference is the same reason why one is a protected class and one isn&amp;#x27;t.&lt;p&gt;The real concern here isn&amp;#x27;t discriminating against people with Hotmail accounts. On the surface level, there&amp;#x27;s nothing wrong with that. This does become an issue if the property of having a Hotmail account is used as an avenue to discriminate against people based on their membership in a protected class -- a sort of actuarial parallel construction, if you will. At that point, we do have a bit of a problem.&lt;p&gt;&amp;gt; We need regulation in place to stop any punitive decision making, public and private, which can be found in court to be based on correlation instead of causation.&lt;p&gt;So, young men could not be charged more for insurance than young women? In fact, young people in general would have to get the same price as everyone else? People in New York would have to pay the same as someone in rural Alaska? Someone who&amp;#x27;s been driving a year gets charged the same as someone who has been driving fifteen, etc.? Even being in an accident doesn&amp;#x27;t &lt;i&gt;cause&lt;/i&gt; you to get in another accident. It&amp;#x27;s just correlated with that outcome. Throw out the entire actuarial aspect of insurance? These &amp;quot;punitive&amp;quot; charges are all based on correlation. I don&amp;#x27;t see this happening.&lt;p&gt;----&lt;p&gt;That said, as a non-statistician, I&amp;#x27;m interested in what kinds of tests can be run on signals to verify that they&amp;#x27;re not proxies for selecting and discriminating against protected classes. Is there a way to modify a signal using its correlation with membership in these classes such that you can marginalize the attributes you&amp;#x27;re not allowed to discriminate based on?&lt;p&gt;For example, it seems reasonable to believe that hotmail use is correlated with age, race, gender, etc. As an insurance company, I would have information on hand to inform me as to the extent of this correlation, at least among my customer base, and I would in turn know the correlations of those factors with risk. Could I somehow remove the racial, gender, and age components of the Hotmail risk signal to obtain a signal that only conveys the portion of the risk correlation that is not based on those classes? If so, what is that statistical technique called?</text></item><item><author>013a</author><text>Its obvious why this is happening; some form of statistical modeling or AI correlated having a hotmail account with being higher risk.&lt;p&gt;This is a serious problem with all AI that makes decisions like this. Having a Hotmail account is a symptom of a problem which could lead to higher risk, but it is judging that symptom as if it is a cause which leads to risk.&lt;p&gt;Its exactly similar to the supposition that black people statistically commit more crime, and thus should pre-emptively receive harsher bail or be profiled. Unless you can scientifically prove that ethnicity causes crime to happen, its disgusting. This is obvious to us.&lt;p&gt;In year&amp;#x27;s past, correlation fallacies might have meant that black people were profiled by police more. Today, it means that we build AIs which make life-ending decisions like determining repeat offender risk, visa status, employment decisions, all of which predetermines an unknown outcome by correlating your known qualities with the known outcome of people with similar qualities to you.&lt;p&gt;We need regulation in place to stop any punitive decision making, public and private, which can be found in court to be based on correlation instead of causation.</text></item></parent_chain><comment><author>discoursism</author><text>I can&amp;#x27;t say for certain, because I wasn&amp;#x27;t involved in the decision. ;) My gut reaction is that it only ends up being seen as OK because the group being penalized has historically been at an advantage in most other areas financially. I am aware that some European countries are moving to do away with charging by gender.</text></comment>