[{"data":1,"prerenderedAt":443},["ShallowReactive",2],{"slug-choosing-between-life-and-death-during-covid-19-the-a-i-trolley-problem":3},{"post":4,"relatedPosts":186,"relatedBooks":347},{"id":5,"date":6,"date_gmt":7,"guid":8,"modified":10,"modified_gmt":11,"slug":12,"status":13,"type":14,"link":15,"title":16,"content":18,"excerpt":21,"author":23,"featured_media":24,"comment_status":25,"ping_status":25,"sticky":26,"template":27,"format":28,"meta":29,"categories":30,"tags":36,"project_category":40,"contact_email_category":45,"yst_prominent_words":46,"class_list":67,"better_featured_image":86,"acf":121,"yoast_meta":130,"_links":133},28033,"2020-05-14T10:00:00","2020-05-14T08:00:00",{"rendered":9},"https:\u002F\u002Fmedicalfuturist.com\u002F?post_id=28033&_wpnonce=8215348fae&status=auto-draft&type=post","2021-01-21T09:56:15","2021-01-21T08:56:15","choosing-between-life-and-death-during-covid-19-the-a-i-trolley-problem","publish","post","https:\u002F\u002Fmedicalfuturist.com\u002Fchoosing-between-life-and-death-during-covid-19-the-a-i-trolley-problem",{"rendered":17},"Choosing Between Life and Death During COVID-19: The A.I. Trolley Problem",{"rendered":19,"protected":20},"\n\u003Cp>Suppose you’re the sole witness of a trolley that has gone out of control, hurtling towards 5 people tied to its track, with no way to stop it in time. Good news: there’s a lever you can pull to alter its direction. Bad news: the other track isn’t safe either as it has one person tied to it. What will you do in this situation? Let the trolley continue on its initial course and kill those 5 people on the way or pull the lever to save them at the expense of that other person’s life?\u003C\u002Fp>\n\n\n\n\u003Cdiv class=\"wp-block-image\">\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"645\" height=\"429\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002Ftrolley-problem.jpg\" alt=\"\" class=\"wp-image-28035\"\u002F>\u003Cfigcaption>Source: https:\u002F\u002Fwww.lionsroar.com\u002F\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\n\u003Cp>This ethical thought experiment, known as the Trolley Problem, was put forth by \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fplato.stanford.edu\u002Fentries\u002Fphilippa-foot\u002F\" target=\"_blank\">Philippa Foot\u003C\u002Fa> back in 1967 to challenge different schools of moral thought. \u003Cstrong>In Lodi, Italy, this dilemma is far from theoretical.\u003C\u002Fstrong> Doctors had to decide whom to allocate ICU beds to, due to the shortage of resources amidst the COVID-19 crisis. \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.bbc.com\u002Ffuture\u002Farticle\u002F20200428-coronavirus-how-doctors-choose-who-lives-and-dies\" target=\"_blank\">Dr. Di Bartolomeo reports\u003C\u002Fa> that some patients “are not candidates” for ventilation support due to their old age or frail condition.\u003C\u002Fp>\n\n\n\n\u003Cp>What if, instead of leaving such morally-challenging decisions to a human, an A.I. would handle this burden? Would it alleviate the psychological toll on the medical staff? \u003C\u002Fp>\n\n\n\n\u003Cp>However, with A.I. in the mix, \u003Cstrong>people are more likely to be among those on the tracks rather than the one behind the lever\u003C\u002Fstrong>. This is because the A.I. will be influenced by the couple of hundreds of programmers deciding on its code and feeding it datasets, as well as the authority overseeing its use. The general public in all that? They are far from the lever’s reach.\u003C\u002Fp>\n\n\n\n\u003Cp>How can the contentious issue of the marred tracks for the A.I. trolley be addressed? Can an algorithm bypass this conundrum altogether? Join us as we take a ride along those tracks.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>The choice\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>As the numbers of those infected with COVID-19 climb, hospital beds around the world are also filling up. As a result, certain healthcare institutions are facing a drought of key resources like personal protective equipment, ICU capacities and ventilators. This has prompted authorities to issue ethical guidelines; directing medical staff as to whom to prioritize should it come to that.\u003C\u002Fp>\n\n\n\n\u003Cp>One such guideline \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.nejm.org\u002Fdoi\u002Ffull\u002F10.1056\u002FNEJMsb2005114\" target=\"_blank\">published in the New England Journal of Medicine\u003C\u002Fa> (NEJM) by a collective of academics and physicians recommends, among others, to prioritize frontline medical staff; and, among severely ill patients, the younger ones with fewer coexisting conditions.\u003C\u002Fp>\n\n\n\n\u003Cdiv class=\"wp-block-image\">\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"394\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002Fchoosing-patients-trolley-problem-768x394.jpg\" alt=\"\" class=\"wp-image-28037\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002Fchoosing-patients-trolley-problem-768x394.jpg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002Fchoosing-patients-trolley-problem.jpg 1200w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003Cfigcaption>Source: https:\u002F\u002Fwww.fredhutch.org\u002F\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\n\u003Cp>“\u003Cstrong>Because maximizing benefits is paramount in a pandemic, we believe that removing a patient from a ventilator or an ICU bed to provide it to others in need is also justifiable and that patients should be made aware of this possibility at admission,\u003C\u002Fstrong>” the authors write.\u003C\u002Fp>\n\n\n\n\u003Cp>Another \u003Ca rel=\"noreferrer noopener\" href=\"http:\u002F\u002Fwww.siaarti.it\u002FSiteAssets\u002FNews\u002FCOVID19%20-%20documenti%20SIAARTI\u002FSIAARTI%20-%20Covid-19%20-%20Clinical%20Ethics%20Reccomendations.pdf\" target=\"_blank\">guideline issued to Italian doctors\u003C\u002Fa> notes that “an age limit for the admission to the ICU may ultimately need to be set”. The New York Times \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.nytimes.com\u002F2020\u002F03\u002F31\u002Fus\u002Fcoronavirus-covid-triage-rationing-ventilators.html\" target=\"_blank\">analyzed publicly available guidelines\u003C\u002Fa> for different American states. Some exclude ventilator support to those suffering from neurological impairments and conditions like dementia or AIDS.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Would an A.I. help?\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>We can all imagine how difficult taking such decisions can be; more so for someone who has pledged the Hippocratic Oath. The authors of the NEJM correctly note that this “will be \u003Cstrong>extremely psychologically traumatic for clinicians\u003C\u002Fstrong>; and some clinicians might refuse to do so”. \u003C\u002Fp>\n\n\n\n\u003Cp>Moreover, despite guidelines, \u003Cstrong>there can be outliers that don’t fit those rules\u003C\u002Fstrong>. What if the younger patient has a higher risk of breast cancer than the older woman who is financially supporting her son through university? What if those prioritized have other comorbidities unbeknownst to them or their physician that will lower their lifespan?\u003C\u002Fp>\n\n\n\n\u003Cp>On the other hand, an A. I. could mine for insights based on a patient’s genomic data, their health records and their familial history. It could deem that a patient would react better to a drug under trial; and would benefit from ventilation support until the drug reaches the hospital. Thereby, monitoring this patient’s progression would help even more people down the line; superseding the guidelines which would otherwise exclude that person from priority treatment.\u003C\u002Fp>\n\n\n\n\u003Cdiv class=\"wp-block-image\">\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"768\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002Fcovid-19-data-ai-768x768.jpg\" alt=\"\" class=\"wp-image-28041\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002Fcovid-19-data-ai-768x768.jpg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002Fcovid-19-data-ai-150x150.jpg 150w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002Fcovid-19-data-ai.jpg 1000w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003Cfigcaption>Source: https:\u002F\u002Fwww.nytimes.com\u002F\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\n\u003Cp>However,\u003Cstrong> when it comes to A.I’s decision-making process, bias is not an issue that can be overlooked.\u003C\u002Fstrong> If the data that such an algorithm is fed is influenced from judgemental datasets, ingrained social injustices and individual choices, then \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fa-i-bias-in-healthcare\u002F\" target=\"_blank\">the software’s output will reflect such bias\u003C\u002Fa>. Given that healthcare data is “\u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fqz.com\u002F1367177\u002Fif-ai-is-going-to-be-the-worlds-doctor-it-needs-better-textbooks\u002F\" target=\"_blank\">extremely male and extremely white\u003C\u002Fa>”, an A. I. will factor its decisions based on this demographic, at the detriment of others.\u003C\u002Fp>\n\n\n\n\u003Cp>The A.I. trolley problem incorporates this layer of complexity in its decision making. While it could lift the psychological burden off the medical personnel of whether to switch the lever or not, the algorithm’s built-in ethical framework makes its decision more of a planned action. This brings us to our next topic:\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Who is in control of the lever?\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Back in 2014, researchers at the MIT Media Lab released the \u003Ca rel=\"noreferrer noopener\" href=\"http:\u002F\u002Fmoralmachine.mit.edu\u002F\" target=\"_blank\">Moral Machine\u003C\u002Fa>. This online platform crowdsources the decisions people take when presented with variations of the trolley problem. It allows participants to choose between two outcomes that a self-driving car should follow. The scenarios include information of the soon-to-be victims’ age, gender and socioeconomic status that can influence the participant’s decision. After it gained traction, with over 40 million decisions from millions of people worldwide, the researchers presented their findings in \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-018-0637-6.epdf\" target=\"_blank\">a paper published in Nature in 2018\u003C\u002Fa>.\u003C\u002Fp>\n\n\n\n\u003Cdiv class=\"wp-block-image\">\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"488\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002Fself-driving-car-trolley-problem-768x488.jpg\" alt=\"\" class=\"wp-image-28043\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002Fself-driving-car-trolley-problem-768x488.jpg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002Fself-driving-car-trolley-problem.jpg 1024w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003Cfigcaption>Source: https:\u002F\u002Fwww.aberdeen.com\u002F\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\n\u003Cp>According to their analysis, decisions of the Moral Machine platform’s participants diverged across culture, economics, and geographic location. Those from collectivist cultures like China and Japan, where respect for the elderly is emphasized, were less likely to spare the young over the old. Participants from countries with significant economic inequality showed greater gaps in the fates of the virtual victims based on their social status. Those from individualistic cultures like the UK and US, where the authors noted the emphasis on each individual’s value, were more inclined to spare more lives given all the other choices.\u003C\u002Fp>\n\n\n\n\u003Cp>It might also turn out that a software programme developed in one region of the world might lead to different outcomes than one developed elsewhere. As pointed out in the introduction, in a healthcare setting,\u003Cstrong> most people are more likely to be on the “tracks”\u003C\u002Fstrong>;\u003Cstrong> at the mercy of the A.I.’s decision\u003C\u002Fstrong>. How do we ensure uniformity in this case?\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Would we even need a lever?\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>When it comes to the A.I. trolley problem in deciding whom to support with available medical resources, \u003Cstrong>the issue really boils down to a faulty supply chain and inadequate preparation\u003C\u002Fstrong>. \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fhow-germany-leveraged-digital-health-to-combat-covid-19\u002F\" target=\"_blank\">Germany\u003C\u002Fa> performed early tests and has sufficient intensive care units, which helped in its comparatively better management of the pandemic.\u003C\u002Fp>\n\n\n\n\u003Cp>While \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fqventus.com\u002Fblog\u002Fpredicting-the-effects-of-the-covid-pandemic-on-us-health-system-capacity\u002F\" target=\"_blank\">Qvetus\u003C\u002Fa> has developed an A.I.-based model to help hospitals better manage their resources, a predictive algorithm can help even before healthcare institutions are put under pressure by a public health crisis. BlueDot’s A.I. helped epidemiologists \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fhow-digital-health-technology-can-help-manage-the-coronavirus-outbreak\u002F\" target=\"_blank\">send the first alerts\u003C\u002Fa> of COVID-19’s impending spread. If such A.I.-based tools are used on a larger scale,  prompt action can be taken sooner; without having to overburden the healthcare system or its staff with morally-taxing situations.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"612\" height=\"408\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F04\u002Fsocial-distancing-flatten-the-curve.jpg\" alt=\"\" class=\"wp-image-27413\"\u002F>\u003Cfigcaption>Source: https:\u002F\u002Fwww.istockphoto.com\u002F\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\u003Cp>\u003Cstrong>Ethically-speaking, doctors should never be put in a position where they have to choose between a 40-year-old or a 75-year-old patient to give ventilator support. With proper management and adequately equipped facilities, such choices would not be the topic of discussion.\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>Nevertheless, if we cannot escape such a dilemma, leaving the decision solely to an A. I. might not be the right course of action. A proper one will require the collaborative efforts of the general public, ethicists and the A.I.’s programmers. If there are demands over more transparent functioning of software behind such a sensitive issue, then these demands will have to be met.\u003C\u002Fp>\n",false,{"rendered":22,"protected":20},"\u003Cp>Let the trolley continue on its initial course and kill those 5 people on the way or pull the lever to save them at the expense of that other person’s life?\u003C\u002Fp>\n",16,28105,"closed",true,"","standard",{"_acf_changed":20,"footnotes":27},[31,32,33,34,35],6479,504,511,521,800,[37,38,39],144,229,3085,[41,42,43,44],947,950,951,952,[],[47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66],4049,4095,4051,1583,4053,1661,4071,1683,4073,1723,4077,1777,4079,3075,4081,4089,3367,4091,3491,4093,[68,14,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85],"post-28033","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-covid-19","category-artificial-intelligence","category-bioethics","category-future-medicine","category-healthcare-policy","tag-artificial-intelligence","tag-ethics","tag-covid19","project_category-company","project_category-medical-professionals","project_category-patients","project_category-policy-makers",{"id":24,"alt_text":87,"caption":88,"description":89,"media_type":90,"media_details":91,"post":5,"source_url":120},"The COVID-trolley problem","There are many decisions that A.I. simply cannot make well enough.","Ethically, there is no good choice for a doctor when he\u002Fshe has to choose between patients about who gets the ventilator. Can AI help?","image",{"width":92,"height":93,"file":94,"sizes":95,"image_meta":117},2000,1125,"2020\u002F05\u002F167_v2-01-1.png",{"medium":96,"large":102,"thumbnail":107,"medium_large":111,"1536x1536":112},{"file":97,"width":98,"height":99,"mime-type":100,"source_url":101},"167_v2-01-1-370x208.png",370,208,"image\u002Fpng","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002F167_v2-01-1-370x208.png",{"file":103,"width":104,"height":105,"mime-type":100,"source_url":106},"167_v2-01-1-768x432.png",768,432,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002F167_v2-01-1-768x432.png",{"file":108,"width":109,"height":109,"mime-type":100,"source_url":110},"167_v2-01-1-150x150.png",150,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002F167_v2-01-1-150x150.png",{"file":103,"width":104,"height":105,"mime-type":100,"source_url":106},{"file":113,"width":114,"height":115,"mime-type":100,"source_url":116},"167_v2-01-1-1536x864.png",1536,864,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002F167_v2-01-1-1536x864.png",{"aperture":118,"credit":27,"camera":27,"caption":27,"created_timestamp":118,"copyright":27,"focal_length":118,"iso":118,"shutter_speed":118,"title":27,"orientation":118,"keywords":119},"0",[],"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002F167_v2-01-1.png",{"cta_type":122,"cta_color":27,"subtitle":27,"related_books":123,"related_posts_footer":127,"related_posts":20},"subscribe",[124,125,126],24762,25985,27927,[128,129],27591,24878,{"yoast_wpseo_title":131,"yoast_wpseo_metadesc":132,"yoast_wpseo_canonical":15},"Choosing Between Life and Death During COVID-19: The A.I. Trolley Problem - The Medical Futurist","Ethically, there is no good choice for a doctor when he\u002Fshe has to choose between two patients on who gets the ventilator. Can A.I. be part of the solution?",{"self":134,"collection":140,"about":143,"author":146,"replies":149,"version-history":152,"predecessor-version":156,"wp:featuredmedia":160,"wp:attachment":163,"wp:term":166,"curies":182},[135],{"href":136,"targetHints":137},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F28033",{"allow":138},[139],"GET",[141],{"href":142},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts",[144],{"href":145},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftypes\u002Fpost",[147],{"embeddable":26,"href":148},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fusers\u002F16",[150],{"embeddable":26,"href":151},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcomments?post=28033",[153],{"count":154,"href":155},10,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F28033\u002Frevisions",[157],{"id":158,"href":159},28159,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F28033\u002Frevisions\u002F28159",[161],{"embeddable":26,"href":162},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia\u002F28105",[164],{"href":165},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia?parent=28033",[167,170,173,176,179],{"taxonomy":168,"embeddable":26,"href":169},"category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcategories?post=28033",{"taxonomy":171,"embeddable":26,"href":172},"post_tag","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftags?post=28033",{"taxonomy":174,"embeddable":26,"href":175},"project_category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fproject_category?post=28033",{"taxonomy":177,"embeddable":26,"href":178},"contact_email_category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcontact_email_category?post=28033",{"taxonomy":180,"embeddable":26,"href":181},"yst_prominent_words","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fyst_prominent_words?post=28033",[183],{"name":184,"href":185,"templated":26},"wp","https:\u002F\u002Fapi.w.org\u002F{rel}",[187],{"id":129,"date":188,"date_gmt":189,"guid":190,"modified":192,"modified_gmt":193,"slug":194,"status":13,"type":14,"link":195,"title":196,"content":198,"excerpt":200,"author":202,"featured_media":203,"comment_status":25,"ping_status":25,"sticky":26,"template":27,"format":28,"meta":204,"categories":205,"tags":206,"project_category":226,"contact_email_category":227,"yst_prominent_words":228,"class_list":236,"better_featured_image":257,"acf":291,"yoast_meta":301,"_links":304},"2019-09-19T16:59:30","2019-09-19T14:59:30",{"rendered":191},"https:\u002F\u002Fmedicalfuturist.com\u002F?post_id=24878&_wpnonce=63e7ccbf7c&status=auto-draft&type=post","2023-03-22T18:35:38","2023-03-22T17:35:38","a-i-bias-in-healthcare","https:\u002F\u002Fmedicalfuturist.com\u002Fa-i-bias-in-healthcare",{"rendered":197},"A.I. Bias In Healthcare",{"rendered":199,"protected":20},"\n\u003Ctable style=\"width: 100%; border-collapse: collapse; background-color: #eee; border-top: 4px solid #444;\" cellpadding=\"5px\">\n\u003Ctbody>\n\u003Ctr>\n\u003Ctd style=\"width: 100%;\">\n\u003Cp style=\"color: #555;font-size: 12px;line-height: 14px\">THIS ARTICLE HAS NOT BEEN UPDATED SINCE 2019. THE INFORMATION SHARED IN THE ARTICLE WAS ACCURATE AT THE TIME OF ITS PUBLICATION, BUT IT MAY BE OUT OF DATE NOW. \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fmagazine\">BROWSE OUR LATEST ARTICLES HERE\u003C\u002Fa>\u003C\u002Fp>\n\u003C\u002Ftd> \u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\n\n\n\n\u003Cp>Logical, reasoned, and rational masterpieces of human intelligence that are assumed to make objective, logical, reasoned decisions and choices. Instead, what the scientific community had to follow lately was how praised smart algorithms proved to be just as biased and judgmental as their human masters, sometimes even leading to scientifically questionable or discriminatory outcomes. Where does A.I. bias come from, how does it appear in healthcare, and what can we do about it? \u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Are you\nA.I.’s favorite?\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Two years ago, Google came under fire when \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.buzzfeed.com\u002Ffionarutherford\u002Fheres-why-some-people-think-googles-results-are-racist\" target=\"_blank\">research\u003C\u002Fa> had shown that \u003Ca href=\"https:\u002F\u002Ftechcrunch.com\u002F2019\u002F07\u002F25\u002Fbias-in-ai-a-problem-recognized-but-still-unresolved\u002F\">when a user searched online for “hands,” the image results were almost all white\u003C\u002Fa>; but when searching for “black hands,” the pictures were far more derogatory depictions, including a white hand reaching out to offer help to a black one, or black hands working in the earth. Not much changed – if you search for “hands” or “black hands”, you still come up with similar results, although the supportive white hand disappeared. \u003C\u002Fp>\n\n\n\n\u003Cp>Similar racial bias follows through the story of A.I. \u003Cstrong>We could hear from a lot of news outlets how facial recognition software favors white faces, but a \u003Ca href=\"http:\u002F\u002Fgendershades.org\u002F\">study\u003C\u002Fa> out of the MIT Media Lab published in February 2018 actually found that facial-recognition systems from companies like IBM and Microsoft were 11-19 percent more accurate on lighter-skinned individuals\u003C\u002Fstrong>. They were particularly bad at identifying women of color. The smart algorithms were 34 percent less accurate at recognizing darker-skinned females compared to lighter-skinned males. \u003Ca href=\"https:\u002F\u002Fqz.com\u002F1367177\u002Fif-ai-is-going-to-be-the-worlds-doctor-it-needs-better-textbooks\u002F\">In another example\u003C\u002Fa>, when A.I. was implemented in the U.S. criminal justice system to predict recidivism, it was found to disproportionately suggest that black people were \u003Ca href=\"https:\u002F\u002Fwww.propublica.org\u002Farticle\u002Fmachine-bias-risk-assessments-in-criminal-sentencing\">more likely to commit future crimes\u003C\u002Fa>, regardless of how minor their initial offense. \u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>It’s not only racial prejudice, but A.I. algorithms also often discriminate against women, minorities, other cultures, or ideologies\u003C\u002Fstrong>. For example, \u003Ca href=\"https:\u002F\u002Fwww.reuters.com\u002Farticle\u002Fus-amazon-com-jobs-automation-insight\u002Famazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G?zd_source=hrt&zd_campaign=4174&zd_term=arranstewart\">Amazon’s HR department had to stop using their A.I.-based machine learning tool\u003C\u002Fa>, which the company developed for sorting out the best job applicants, as it turned out that the smart algorithm favored men. As the tech scene is mainly dominated by men, and the data that the software was fed contained resumés from the past 10 years, the program taught itself that women were less preferable candidates. While programmers tried to tweak the A.I., it still didn’t bring the expected results, so in the end, they decided to scrap the program entirely. But what happened here? What went wrong with the algorithm? What’s the difficulty with teaching A.I.?\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"512\" height=\"341\" src=\"https:\u002F\u002F1712507217.rsc.cdn77.org\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FBiased-Algorithms-512x341.jpeg\" alt=\"A.I. Bias\" class=\"wp-image-24880\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FBiased-Algorithms-512x341.jpeg 512w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FBiased-Algorithms-768x512.jpeg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FBiased-Algorithms-1536x1024.jpeg 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FBiased-Algorithms.jpeg 2000w\" sizes=\"auto, (max-width: 512px) 100vw, 512px\" \u002F>\u003Cfigcaption class=\"wp-element-caption\">Source: www.entrepreneur.com\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>A quest for\nunbiased cat pictures\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>In order to discern why an A.I. algorithm could be biased, let’s take everyone’s favorite machine learning example: an algorithm recognizing cats in images. You will need millions of photos about all kinds of cats labeled as cats, and feed them to the algorithm, which will eventually learn to categorize the animal – without actually instructing it that cats are furry animals with four legs and two eyes. Such description would anyways exclude sphynx cats or our three-legged buddies. But what if those hairless creatures got ignored for other reasons, too? \u003C\u002Fp>\n\n\n\n\u003Cp>Here are the three main reasons for biased algorithms:\u003C\u002Fp>\n\n\n\n\u003Cp>1.\u003Cstrong>Judgmental data sets\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>Algorithms are trained on datasets, thus the quality of the data is crucial in the process. If the dataset is incomplete, not diverse enough, stems mainly from one area of study, the A.I. software could work flawlessly in the test environment, but come up with its inherent bias in the ‘real world’.\u003C\u002Fstrong> For example, if our cat-spotting algorithm never gets to see any sphynx cat, it will fairly believe that cats are furry – and when eventually encounters a hairless animal, it won’t recognize it. That’s what often happens with facial recognition software\u003C\u002Fp>\n\n\n\n\u003Cp>2. \u003Cstrong>Deeply ingrained social injustices\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>Another, more complex issue is when the dataset is representative and diverse enough, but the algorithm still arrives at discriminative conclusions. The reason for that could be a social practice ingrained so deeply in society that will automatically be transferred into the judgment process of the A.I. \u003C\u002Fstrong>For example, as cats have not worn sweaters for centuries, a smart algorithm might miss out a modern-day cat in a pullover. In a nutshell, that’s the reason for Amazon’s gender-biased HR algorithm: the program was fed with applications from the previous ten years, whose majority came from male candidates. As a consequence, the A.I. started to believe that the correlation between gender and qualifications in this area also meant causation – and a point of reference for selection.\u003C\u002Fp>\n\n\n\n\u003Cp>3. \u003Cstrong>Unconscious or conscious individual choices \u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>And what if the programmer must choose or leave out some parameters to help the program learn? \u003Cstrong>By describing the cat in a certain way – hairiness, color, legs, eyes, etc. -, they already include their hidden and frequently unconscious bias.\u003C\u002Fstrong> When a shelter wants to decide which cat to offer for adoption, how will the parameters look? And turning to people, when banks screen through loan applications with the help of algorithms, who decides who can get the loan? The programmer, the bank, or a human being? In such cases, the software developer can unconsciously include their own values and beliefs about the world into the code, and in an even more sensitive situation, perhaps with even riskier outcomes, the programmer could set some variables selecting specific characteristics for individuals or groups – which might have a biased outcome. Either way, individual choices can greatly influence how smart algorithms ‘behave’.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"512\" height=\"160\" src=\"https:\u002F\u002F1712507217.rsc.cdn77.org\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002F10-21-2018-Digitalist_Q1_AI-Bias_F-512x160.jpg\" alt=\"A.I. Bias\" class=\"wp-image-24879\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002F10-21-2018-Digitalist_Q1_AI-Bias_F-512x160.jpg 512w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002F10-21-2018-Digitalist_Q1_AI-Bias_F-768x240.jpg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002F10-21-2018-Digitalist_Q1_AI-Bias_F-1536x480.jpg 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002F10-21-2018-Digitalist_Q1_AI-Bias_F.jpg 1920w\" sizes=\"auto, (max-width: 512px) 100vw, 512px\" \u002F>\u003Cfigcaption class=\"wp-element-caption\">Source: www.geneticliteracyproject.org\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Health data\nis mostly white and male\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Thus, \u003Cstrong>the source, the quality, and the diversity of the data, the historical social practices ingrained into the data – meaning the bias of the deeper social structure, as well as the individual, conscious or unconscious preferences of individual programmers, determine whether and to what extent an A.I. will become biased.\u003C\u002Fstrong> Now, let’s look at some examples from healthcare where many could believe that as smart algorithms look at medical images, ECG strips or electronic medical records, the “bias factor” must be less prevalent.\u003C\u002Fp>\n\n\n\n\u003Cp>Well, we shall bring some disillusionment. Even \u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=TATSAHJKRd8\">comedian John Oliver said\u003C\u002Fa> that bias in medicine, in general, is a serious issue with consequences for American society. \u003Ca href=\"https:\u002F\u002Fqz.com\u002F1367177\u002Fif-ai-is-going-to-be-the-worlds-doctor-it-needs-better-textbooks\u002F\">Healthcare data is extremely male and extremely white\u003C\u002Fa>, and that has real-world impacts. A 2014 study that \u003Ca href=\"https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Ffull\u002F10.1002\u002Fcncr.28617\">tracked cancer mortality over 20 years\u003C\u002Fa> pointed to a lack of diverse research subjects as a key reason why black Americans are significantly more likely to die from cancer than white Americans. \u003C\u002Fp>\n\n\n\n\u003Cp>In another area of research, \u003Ca href=\"https:\u002F\u002Fwww.nature.com\u002Fnews\u002Fgenomics-is-failing-on-diversity-1.20759\">meta-analysis\u003C\u002Fa> looking at 2,511 studies from around the world found\nthat 81 percent of participants in genome-mapping studies were of European\ndescent. This has severe real-world impacts: researchers who download\npublicly-available data to study disease are far more likely to use the genomic\ndata of people of European descent than those of African, Asian, Hispanic, or\nMiddle Eastern descent. And these distorted datasets would be the starting\npoints for A.I. development. \u003C\u002Fp>\n\n\n\n\u003Cp>Sometimes, ignorance of inherent bias in data could even jeopardize the applicability of an algorithm. \u003Ca href=\"https:\u002F\u002Fwinterlightlabs.com\u002F\">Winterlight Labs\u003C\u002Fa>, a Toronto-based startup, which is building auditory tests for neurological diseases, \u003Ca href=\"https:\u002F\u002Fqz.com\u002F1367177\u002Fif-ai-is-going-to-be-the-worlds-doctor-it-needs-better-textbooks\u002F\">realized after a while that their technology only worked for English speakers of a particular Canadian dialect\u003C\u002Fa>. That might be a serious problem for other companies, too, which are working with \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fvoice-to-text-technologies-shape-the-future\u002F\">voice-to-text technologies\u003C\u002Fa>, \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fvocal-biomarkers-new-opportunities-prevention\u002F\">vocal biomarkers\u003C\u002Fa>, or digital assistants such as Siri or Alexa for healthcare.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"512\" height=\"341\" src=\"https:\u002F\u002F1712507217.rsc.cdn77.org\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FAlgorithmically-biased-512x341.jpg\" alt=\"A.I. Bias\" class=\"wp-image-24883\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FAlgorithmically-biased-512x341.jpg 512w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FAlgorithmically-biased-768x511.jpg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FAlgorithmically-biased.jpg 1500w\" sizes=\"auto, (max-width: 512px) 100vw, 512px\" \u002F>\u003Cfigcaption class=\"wp-element-caption\">Source: www.npr.org\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Getting out of the cognitive cage\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>So, what should we do to eliminate these prejudices\nfrom programming smart algorithms? It is actually a very difficult task as\nhuman beings have their own bias in their thinking – and that has been a useful\ntrait for thousands of years as it shortens the time needed for making snap\ndecisions. It’s also likely that human bias is here to stay, and technologies\nthat are fed by information that is created in the real world \u003Ca href=\"https:\u002F\u002Fwww.zdnet.com\u002Farticle\u002Fwhat-is-bias-in-ai-really-and-why-cant-ai-neutralize-it\u002F\">could fundamentally have the same outcome\u003C\u002Fa>. \u003Cstrong>So now the question is, how do you think about\nthat when you’re actually shifting a cognitive task completely into a machine,\nwhere you don’t have the same kind of qualitative reaction that human beings\nwill have? \u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>The response might be twofold and still evolving. \u003C\u002Fp>\n\n\n\n\u003Col class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>We\nhave to raise awareness of inherent bias in algorithms\u003C\u002Fstrong>. It’s a great step that is already applied in some\nplaces. Recently, \u003Ca href=\"https:\u002F\u002Fwww.bbc.com\u002Fnews\u002Ftechnology-49717378\">police officers have raised concerns about using\n“biased” artificial-intelligence tools\u003C\u002Fa>, a report commissioned by one of the UK government’s\nadvisory bodies revealed. The report said policemen were worried about both\ndata bias and becoming more reliant on automation. Another similar example was banning \u003Ca href=\"https:\u002F\u002Fwww.hrtechnologist.com\u002Farticles\u002Fdiversity\u002Fwhy-is-artificial-intelligence-biased-against-women\u002F\">facial recognition software from the streets of San\nFrancisco\u003C\u002Fa>. Activists and\npoliticians, who pushed for the ordinance, cited studies that showed A.I.-based\nfacial recognition technology is less accurate when distinguishing between\nindividual women and people of color.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>We\nmight have to re-create these functions, such as facial recognition technology,\nto represent a more balanced attitude through minimizing bias\u003C\u002Fstrong>. That’s a tricky and a difficult process, especially\nbecause most A.I. algorithms are trained on biased datasets and researchers are\njust starting to bring them to the real-world. \u003C\u002Fli>\n\u003C\u002Fol>\n\n\n\n\u003Cp>Also, in many cases, it must be difficult to admit how biased we, human beings, are, and it’s kind of embarrassing that machines are pointing that out for us. But well, at least, we hope we are learning something about ourselves and how to make the world a less biased place. That would just be fantastic.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image\">\u003Cimg decoding=\"async\" src=\"https:\u002F\u002F1712507217.rsc.cdn77.org\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FAlgorithmic-bias-1-512x288.jpg\" alt=\"A.I. Bias\" class=\"wp-image-24882\"\u002F>\u003Cfigcaption class=\"wp-element-caption\">Source: www.towardsdatascience.com\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\u003Ctable style=\"width: 100%; border-collapse: collapse; background-color: #eee; border-top: 4px solid #444;\" cellpadding=\"10px\">\n\u003Ctbody>\n\u003Ctr>\n\u003Ctd style=\"width: 100%;\">\n\u003Cp>\u003Cstrong>At The Medical Futurist, we are building a community for making a bold vision about the future of healthcare reality today.\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Ciframe loading=\"lazy\" width=\"560\" height=\"315\" src=\"https:\u002F\u002Fwww.youtube.com\u002Fembed\u002FFIbMejImnxs\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen=\"\">\u003C\u002Fiframe>\n\u003Cp>If you’d like to support this mission, we invite you to \u003Ca href=\"https:\u002F\u002Fwww.patreon.com\u002Fthemedicalfuturist\">join The Medical Futurist Patreon Community\u003C\u002Fa>. A community of empowered patients, future-oriented healthcare professionals, concerned health policymakers, sensible health tech developers, and enthusiastic medical students. If there were ever a time to join us, it is now. Every contribution, however big or small, powers our research and sustains our future.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.patreon.com\u002Fthemedicalfuturist\">\u003Cstrong style=\"background-color: #e71d3299; color: #000;\">Click here to support The Medical Futurist from as little as $3\u003C\u002Fstrong>\u003C\u002Fa> – it only takes a minute. Thank you.\u003C\u002Fp>\n\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\n\u003C\u002Ftable>\n\u003Cp> \u003C\u002Fp>\n",{"rendered":201,"protected":20},"\u003Cp>Logical, reasoned, and rational masterpieces of human intelligence that are assumed to make objective, logical, reasoned decisions and choices. Instead, what the scientific community had to follow lately was how praised smart algorithms proved to be just as biased and judgmental as their human masters, sometimes even leading to scientifically questionable or discriminatory outcomes. Where does A.I. bias come from, how does it appear in healthcare, and what can we do about it? \u003C\u002Fp>\n",6,24884,{"_acf_changed":20,"footnotes":27},[32,34],[207,208,209,210,37,211,38,212,213,214,215,216,217,218,219,220,221,222,223,224,225],671,1478,137,926,1104,1168,246,1203,271,1228,275,1245,425,1474,1475,1476,636,1477,134,[],[],[52,229,230,231,232,233,234,235],1693,1819,1883,2689,2705,2739,3657,[237,14,69,70,71,72,73,75,77,238,239,240,241,79,242,80,243,244,245,246,247,248,249,250,251,252,253,254,255,256],"post-24878","tag-machine-learning","tag-ethnic","tag-algorithm","tag-society","tag-genetic","tag-smart-algorithm","tag-future","tag-smart","tag-health","tag-artificial","tag-healthcare","tag-gender","tag-technology-2","tag-bias","tag-prejudice","tag-judgement","tag-deep-learning","tag-racial","tag-ai",{"id":203,"alt_text":258,"caption":27,"description":27,"media_type":90,"media_details":259,"post":129,"source_url":290},"A.I. 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