{"id":786,"date":"2019-10-15T11:11:07","date_gmt":"2019-10-15T11:11:07","guid":{"rendered":"http:\/\/jessicadoeshistory.com\/courseblog\/?p=786"},"modified":"2019-11-12T19:26:59","modified_gmt":"2019-11-12T19:26:59","slug":"unfair-algorithms","status":"publish","type":"post","link":"https:\/\/jessicadoeshistory.com\/courseblog\/critical\/unfair-algorithms\/","title":{"rendered":"Unfair Algorithms"},"content":{"rendered":"\n

Algorithm criticism is based on the idea that an algorithm is not in fact neutral as most people believe, but is actually \u201c opinion embedded in math\u201d<\/span>1<\/sup><\/a><\/span> . Because algorithms are built by humans they have the same biases and prejudices that we have. Scholars and researchers like Cathy O\u2019Neil and Louise Matsakis are calling for greater transparency and standards in the world of algorithms and artificial intelligence. In her article \u201cWhat Does a Fair Algorithm Actually Look Like?\u201d Louise Matsakis details that many people don\u2019t understand how an algorithm works <\/span>2<\/sup><\/a><\/span>. They may have been declined a bank loan, or health benefits based on a computer algorithm meant to weed out potentially more at risk applicants. But by making the process that goes into designing these algorithms more open it would help people to better understand what they could do to succeed in or appeal those decisions.\u00a0<\/p>\n\n\n\n

Safiya Noble\u2019s Algorithms of Oppression: How Search Engines Reinforce Racism<\/em> excerpts illustrate how people of color are being oppressed in Google\u2019s search results in a parallel to how they were treated in the early part of the 20th century <\/span>3<\/sup><\/a><\/span>. We saw musicians like Lead Belly dressed in costumes meant to reinforce ideas of how people of color were perceived as \u201cless than\u201d their white counterparts. In a similar matter when Noble entered the phrase \u201cblack girls\u201d into the Google search bar the autofill suggestions were tantamount to porn results. Again the racial perception being that when searching for anything related to \u201cblack girls\u201d the searcher would be looking for less than desirable traits, or behaviors. The sexualization of black women is propagated with the expectation of promiscuous behavior associated with them. This is racism that began early in American history and has been carried down through generations.\u00a0<\/p>\n\n\n\n

So how can we change this? O\u2019Neil says that people need to \u201cquestion the algorithms themselves and\u00a0 build ethics into them\u201d<\/span>4<\/sup><\/a><\/span>. Matsakis states that if people knew that by changing one small factor- or if the people using the algorithms understood which factors are playing into the decisions- the outcomes of these decisions could be changed. The outcomes would be more fair. The idea that someone\u2019s zip code alone would preclude them from obtaining a loan is ridiculous, but it happens. I think that if consumers better understood these things they could more easily demand transparency, and if that demand isn\u2019t met take their business to someone else. If there was a standard for ethics in algorithmic programming things like this would happen less often. I think that modern digital consumers are helping to change the attitudes and practices in their algorithmic designs. As more of our lives are intrinsically intertwined with computers the idea of using a computer program to weed through data is common. Thinking back to when Tony Guidone <\/span>5<\/sup><\/a><\/span> came to talk to us about key word searches, I feel like there is a connection between using the right kind of keywords when searching for information and creating algorithms. We may not be automating them and turning our decisions into code but we are still using a process of elimination in the same way that algorithms do.\u00a0<\/p>\n\n\n\n

I thought that algorithms could only be processed by computers, but now I think that we are also computers and process them constantly.
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Algorithm criticism is based on the idea that an algorithm is not in fact neutral as most people believe, but is actually \u201c opinion embedded in math\u201d . Because algorithms are built by humans they have the same biases and prejudices that we have. Scholars and researchers like Cathy O\u2019Neil and Louise Matsakis are calling … Continue reading “Unfair Algorithms”<\/span><\/a><\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5],"tags":[26],"class_list":["post-786","post","type-post","status-publish","format-standard","hentry","category-critical","tag-fall-2019"],"_links":{"self":[{"href":"https:\/\/jessicadoeshistory.com\/courseblog\/wp-json\/wp\/v2\/posts\/786","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/jessicadoeshistory.com\/courseblog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/jessicadoeshistory.com\/courseblog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/jessicadoeshistory.com\/courseblog\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/jessicadoeshistory.com\/courseblog\/wp-json\/wp\/v2\/comments?post=786"}],"version-history":[{"count":3,"href":"https:\/\/jessicadoeshistory.com\/courseblog\/wp-json\/wp\/v2\/posts\/786\/revisions"}],"predecessor-version":[{"id":961,"href":"https:\/\/jessicadoeshistory.com\/courseblog\/wp-json\/wp\/v2\/posts\/786\/revisions\/961"}],"wp:attachment":[{"href":"https:\/\/jessicadoeshistory.com\/courseblog\/wp-json\/wp\/v2\/media?parent=786"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/jessicadoeshistory.com\/courseblog\/wp-json\/wp\/v2\/categories?post=786"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/jessicadoeshistory.com\/courseblog\/wp-json\/wp\/v2\/tags?post=786"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}