A Discriminatory Algorithm Wrongly Accused Thousands of Families of Fraud
Adding this to my running list of algorithms encoding and amplifying systemic bias, like:<p>* A hospital AI algorithm discriminating against Black people when providing additional healthcare outreach by amplifying racism already in the system. <a href="https:&#x2F;&#x2F;www.nature.com&#x2F;articles&#x2F;d41586-019-03228-6" rel="nofollow">https:&#x2F;&#x2F;www.nature.com&#x2F;articles&#x2F;d41586-019-03228-6</a><p>* Misdiagnosing people of African decent with genomic variants misclassified as pathogenic due to most of our reference data coming from European&#x2F;white males. <a href="https:&#x2F;&#x2F;www.nejm.org&#x2F;doi&#x2F;full&#x2F;10.1056&#x2F;NEJMsa1507092" rel="nofollow">https:&#x2F;&#x2F;www.nejm.org&#x2F;doi&#x2F;full&#x2F;10.1056&#x2F;NEJMsa1507092</a><p>* When the dangers of ML in diagnosing Melanoma exacerbating healthcare disparities for darker skinned people. <a href="https:&#x2F;&#x2F;jamanetwork.com&#x2F;journals&#x2F;jamadermatology&#x2F;article-abstract&#x2F;2688587" rel="nofollow">https:&#x2F;&#x2F;jamanetwork.com&#x2F;journals&#x2F;jamadermatology&#x2F;article-abs...</a><p>* When Google&#x27;s hate speech detecting AI inadvertantly censored anyone who used vernacular referred to in this article as being &quot;African American English&quot;. <a href="https:&#x2F;&#x2F;fortune.com&#x2F;2019&#x2F;08&#x2F;16&#x2F;google-jigsaw-perspective-racial-bias&#x2F;" rel="nofollow">https:&#x2F;&#x2F;fortune.com&#x2F;2019&#x2F;08&#x2F;16&#x2F;google-jigsaw-perspective-rac...</a><p>* When Amazon&#x27;s AI recruiting tool inadvertantly filtered out resumes from women. <a href="https:&#x2F;&#x2F;www.reuters.com&#x2F;article&#x2F;us-amazon-com-jobs-automation-insight&#x2F;amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G" rel="nofollow">https:&#x2F;&#x2F;www.reuters.com&#x2F;article&#x2F;us-amazon-com-jobs-automatio...</a><p>* When AI criminal risk prediction software used by judges in deciding the severity of punishment for those convicted predicts a higher chance of future offence for a young, Black first time offender than for an older white repeat felon. <a href="https:&#x2F;&#x2F;www.propublica.org&#x2F;article&#x2F;machine-bias-risk-assessments-in-criminal-sentencing" rel="nofollow">https:&#x2F;&#x2F;www.propublica.org&#x2F;article&#x2F;machine-bias-risk-assessm...</a>
And here&#x27;s some good news though:<p>* When police wrongfully arrested a person based on faulty facial recognition match using grainy security camera footage, without any due diligence, asking for an alibi, or any other investigation. <a href="https:&#x2F;&#x2F;www.npr.org&#x2F;2020&#x2F;06&#x2F;24&#x2F;882683463&#x2F;the-computer-got-it-wrong-how-facial-recognition-led-to-a-false-arrest-in-michig" rel="nofollow">https:&#x2F;&#x2F;www.npr.org&#x2F;2020&#x2F;06&#x2F;24&#x2F;882683463&#x2F;the-computer-got-it...</a><p>* When the above is compounded for people of color according to studies which show that facial recognition systems misidentify dark-skinned women 40x more often than for light-skinned men. <a href="http:&#x2F;&#x2F;news.mit.edu&#x2F;2018&#x2F;study-finds-gender-skin-type-bias-artificial-intelligence-systems-0212" rel="nofollow">http:&#x2F;&#x2F;news.mit.edu&#x2F;2018&#x2F;study-finds-gender-skin-type-bias-a...</a>. Another study showed false positives can be 10x to 100x more frequent for Asian and African American faces compared to Caucasian. <a href="https:&#x2F;&#x2F;www.nist.gov&#x2F;news-events&#x2F;news&#x2F;2019&#x2F;12&#x2F;nist-study-evaluates-effects-race-age-sex-face-recognition-software" rel="nofollow">https:&#x2F;&#x2F;www.nist.gov&#x2F;news-events&#x2F;news&#x2F;2019&#x2F;12&#x2F;nist-study-eva...</a><p>* When an algorithm blocked kidney transplants for Black patients. <a href="https:&#x2F;&#x2F;www.wired.com&#x2F;story&#x2F;how-algorithm-blocked-kidney-transplants-black-patients&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.wired.com&#x2F;story&#x2F;how-algorithm-blocked-kidney-tra...</a><p>* When clinical algorithms include “corrections” for race which directly raise the bar for the need for interventions in people of color, such that they then receive less clinical screening, less surveillance, less diagnoses, and less treatment for everything, including cancer, organ transplants, birth interventions, urinary and blood, bone, and heart disease. <a href="https:&#x2F;&#x2F;www.nejm.org&#x2F;doi&#x2F;10.1056&#x2F;NEJMms2004740" rel="nofollow">https:&#x2F;&#x2F;www.nejm.org&#x2F;doi&#x2F;10.1056&#x2F;NEJMms2004740</a>