{"id":1422,"date":"2020-09-08T08:43:14","date_gmt":"2020-09-08T08:43:14","guid":{"rendered":"http:\/\/horizon.peachpuff-wolverine-566518.hostingersite.com\/?p=1422"},"modified":"2020-09-08T08:43:14","modified_gmt":"2020-09-08T08:43:14","slug":"encoding-the-same-biases-artificial-intelligences-limitations-in-coronavirus-response","status":"publish","type":"post","link":"https:\/\/scienceblog.com\/horizon\/1422\/encoding-the-same-biases-artificial-intelligences-limitations-in-coronavirus-response\/","title":{"rendered":"\u2018Encoding the same biases\u2019: Artificial intelligence\u2019s limitations in coronavirus response"},"content":{"rendered":"
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As the coronavirus pandemic endures, the socio-economic implications of race and gender in contracting Covid-19 and dying from it have been laid bare. Artificial intelligence (AI) is playing a key role in the response, but it could also be exacerbating inequalities within our health systems \u2013 a critical concern that is dragging the technology\u2019s limitations back into the spotlight.<\/strong><\/p>\n<\/div>\n<\/div>\n<\/div>\n

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The response to the crisis has in many ways been mediated by data \u2014\u00a0an explosion of information being used by AI algorithms\u00a0to better understand and address Covid-19<\/a>, including tracking the virus\u2019 spread and\u00a0developing therapeutic interventions<\/a>.<\/p>\n

AI, like its human maker, is not immune to bias. The technology \u2014\u00a0generally designed to digest large volumes of data and make deductions to support decision making \u2014\u00a0reflects the prejudices of the humans who develop it and feed it information that it uses to spit out outcomes. For example, years ago when Amazon developed an AI tool to help rank job candidates by learning from its past hires, the system mimicked the gender-bias of its makers by\u00a0downgrading resumes from women<\/a>.<\/p>\n

\u2018We were seeing AI being used extensively before Covid-19, and during Covid-19 you’re seeing an increase in the use of some types of tools,\u2019 noted Meredith Whittaker, a distinguished research scientist at New York University in the US and co-founder of\u00a0AI Now<\/a>\u00a0Institute, which carries out research examining the social implications of AI.<\/p>\n

Monitoring tools<\/a>\u00a0to keep an eye on white collar workers working from home and educational tools that claim to detect whether students are cheating in exams are increasingly growing common. But Whittaker says that most of this technology is untested \u2013 and some has been shown to be flawed. However, that hasn\u2019t stopped companies from marketing their products as cure-alls for the collateral damage caused by the pandemic, she adds.<\/p>\n

In the US for instance, a compact medical device called a\u00a0pulse oximeter<\/a>, designed to gauge the level of oxygen in the blood,\u00a0had some coronavirus patients glued to its tiny screens to decide when to go to the hospital, in addition to its use by doctors to aid in\u00a0clinical decision making within hospitals<\/a>.<\/p>\n

The way the device works, however, is prone to racial bias and was likely calibrated on light skinned users. Back in 2005, a study definitively showed the device \u2018mostly tended to\u00a0overestimate<\/a>\u00a0(oxygen) saturation levels by several points\u2019 for non-white people.<\/p>\n

The problem with the pulse oximeter device has been known for decades and hasn\u2019t been fixed by manufacturers, says Whittaker. \u2018But, even so, these tools are being used, they’re producing data and that data is going on to shape diagnostic algorithms that are used in health care. And so, you see, even at the level of how our AI systems are constructed, they’re encoding the same biases and the same histories of racism and discrimination that are being shown so clearly in the context of Covid-19.\u2019<\/p>\n

Evidence<\/strong><\/p>\n

Meanwhile, as the body of evidence accumulates that people of\u00a0colour are more likely to die from Covid-19 infections<\/a>, that diversity has not necessarily been\u00a0reflected in the swathe of clinical trials<\/a>\u00a0christened to develop drugs and vaccines \u2014\u00a0a troubling pattern that has long preceded the pandemic. When it comes to gender diversity, a recent review found that of 927 trials related to Covid-19, more than half explicitly excluded pregnancy, and\u00a0pregnant women have been excluded altogether from vaccine trials<\/a>.<\/p>\n

The outcomes of products in these clinical trials will not necessarily be representative of the population, notes Catelijne Muller, a member of an EU high-level expert group on AI and co-founder of ALLAI, an organisation dedicated to fostering responsible AI.<\/p>\n

\u2018And if you then use those outcomes to feed an AI algorithm for future predictions, those people will also have a disadvantage in these prediction models,\u2019 she said.<\/p>\n

The trouble with use of AI technology in the context of Covid-19 is not different from the issues of bias that plagued the technology before the pandemic: if you feed the technology biased data, it will spout biased outcomes. Indeed, existing large-scale AI systems also reflect the lack of diversity in the environments in which they are built and the people who have built them. These are almost exclusively a handful of technology companies and elite university laboratories \u2013 \u2018spaces that in the West tend to be\u00a0extremely white, affluent, technically oriented, and male<\/a>,\u2019 according to a 2019 report by the AI Now Institute.<\/p>\n

But the technology isn\u2019t simply a reflection of its makers \u2014\u00a0AI also amplifies their biases, says Whittaker.<\/p>\n

\u2018One person may have biases, but they don’t scale those biases to millions and billions of decisions,\u2019 she said. \u2018Whereas an AI system can encode human biases and then can distribute those in ways that have a much greater impact.\u2019<\/p>\n

Complicating matters further, there are automation bias concerns, she adds. \u2018There is a tendency for people to be more trusting of a decision that is made by a computer than they are of the same decision if it were made by a person. So, we need to watch out for the way in which AI systems launder these biases and make them seem rigorous and scientific and may lead to people being less willing to question decisions made by these systems.\u2019<\/p>\n

\u2018We need to watch out for the way in which AI systems launder these biases and make them seem rigorous and scientific.\u2019<\/p>\n

-Meredith Whittaker, New York University, US<\/p><\/blockquote>\n

Safe<\/strong><\/p>\n

There is no clear consensus on what will make AI technology responsible and safe en masse, experts say, though researchers are beginning to agree on useful steps such as fairness, interpretability and robustness.<\/p>\n

The first step is to ask \u2018question zero\u2019, according to Muller: what is my problem and how can I solve it? Do I solve it with artificial intelligence or with something else? If with AI, is this application good enough? Does it harm fundamental rights?<\/p>\n

\u2018What we see is that many people think that sometimes AI is sort of a magic wand\u2026and it’ll kind of solve everything. But sometimes it doesn’t solve anything because it’s not fit for the problem. Sometimes it’s so invasive that it might solve one problem, but create a large, different problem.\u2019<\/p>\n

When it comes to using AI in the context of Covid-19 \u2014\u00a0there is an eruption of data, but that data needs to be reliable and be optimised, says Muller.<\/p>\n

\u2018Data cannot just be thrown at another algorithm\u2019 she said, explaining that algorithms work by finding correlations. \u2018They don’t understand what a virus is.\u2019<\/p>\n

Fairness issues with AI showcase the biases in human decision making, according to Dr Adrian Weller, programme director for AI at the Alan Turing Institute in the UK. It\u2019s wrong to assume that not using algorithms means everything will be just fine, he says.<\/p>\n

There is this hope and excitement about these systems because they operate more consistently and efficiently than humans, but they lack notions of common sense, reasoning and context, where humans are much better, Weller says.<\/p>\n

Accountability<\/strong><\/p>\n

Having humans partake more in the decision-making process is one way to bring accountability to AI applications. But figuring out who that person or persons should be is crucial.<\/p>\n

\u2018Simply putting a human somewhere in the process does not guarantee a good decision,\u2019 said Whittaker. There are issues such as who that human works for and what incentives they’re working under which need to be addressed, she says.<\/p>\n

\u2018I think we need to really narrow down that broad category of \u201chuman\u201d and look at who and to what end.\u2019<\/p>\n

Human oversight could be incorporated in multiple ways care to ensure transparency and mitigate bias, suggest ALLAI\u2019s Muller and colleagues in a\u00a0report<\/a>\u00a0analysing a\u00a0proposal EU regulators<\/a>\u00a0are working on to regulate \u2018high-risk\u2019 AI applications such as for use in recruitment, biometric recognition or in the deployment of health.<\/p>\n

These include auditing every decision cycle of the AI system, monitoring the operation of the system, having the discretion to decide when and how to use the system in any particular situation, and the opportunity to override a decision made by a system.<\/p>\n

For Whittaker, recent developments such as EU regulators’ willingness to regulate \u2018high-risk\u2019 applications or community organising in the US leading to bans on facial recognition technology are encouraging.<\/p>\n

\u2018I think we need more of the same\u2026to ensure that these systems are auditable, that we can examine them to ensure that they are democratically controlled, and that people have a right to refuse the use of these systems.\u2019<\/p>\n

Meredith Whittaker\u00a0<\/em>and Catelijne Muller will be speaking at a panel to discuss tackling gender and ethnicity biases in artificial intelligence at the\u00a0<\/em>European Research and Innovation Days conference<\/em><\/a>\u00a0which will take place online from 22-24 September.<\/em><\/p>\n

Originally published on Horizon magazine<\/a>.<\/em><\/p>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

As the coronavirus pandemic endures, the socio-economic implications of race and gender in contracting Covid-19 and dying from it have been laid bare. Artificial intelligence (AI) is playing a key role in the response, but it could also be exacerbating inequalities within our health systems \u2013 a critical concern that is dragging the technology\u2019s limitations … Read more<\/a><\/p>\n","protected":false},"author":298,"featured_media":1423,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"generate_page_header":"","jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[10,12,118,16],"tags":[48,359,195,37],"class_list":["post-1422","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-brain-behavior","category-health","category-social-sciences","category-technology","tag-ai","tag-coronavirus","tag-gender","tag-health"],"yoast_head":"\n\u2018Encoding the same biases\u2019: Artificial intelligence\u2019s limitations in coronavirus response - Horizon Magazine Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/scienceblog.com\/horizon\/1422\/encoding-the-same-biases-artificial-intelligences-limitations-in-coronavirus-response\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u2018Encoding the same biases\u2019: Artificial intelligence\u2019s limitations in coronavirus response\" \/>\n<meta property=\"og:description\" content=\"As the coronavirus pandemic endures, the socio-economic implications of race and gender in contracting Covid-19 and dying from it have been laid bare. 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This discrepancy has shined a spotlight on a key theme\u2026","rel":"","context":"In "Health"","block_context":{"text":"Health","link":"https:\/\/scienceblog.com\/horizon\/category\/health\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/scienceblog.com\/horizon\/wp-content\/uploads\/sites\/4\/2020\/08\/pexels-ketut-subiyanto-4436372-scaled.jpg?resize=350%2C200&ssl=1","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/scienceblog.com\/horizon\/wp-content\/uploads\/sites\/4\/2020\/08\/pexels-ketut-subiyanto-4436372-scaled.jpg?resize=350%2C200&ssl=1 1x, https:\/\/i0.wp.com\/scienceblog.com\/horizon\/wp-content\/uploads\/sites\/4\/2020\/08\/pexels-ketut-subiyanto-4436372-scaled.jpg?resize=525%2C300&ssl=1 1.5x, https:\/\/i0.wp.com\/scienceblog.com\/horizon\/wp-content\/uploads\/sites\/4\/2020\/08\/pexels-ketut-subiyanto-4436372-scaled.jpg?resize=700%2C400&ssl=1 2x, https:\/\/i0.wp.com\/scienceblog.com\/horizon\/wp-content\/uploads\/sites\/4\/2020\/08\/pexels-ketut-subiyanto-4436372-scaled.jpg?resize=1050%2C600&ssl=1 3x, https:\/\/i0.wp.com\/scienceblog.com\/horizon\/wp-content\/uploads\/sites\/4\/2020\/08\/pexels-ketut-subiyanto-4436372-scaled.jpg?resize=1400%2C800&ssl=1 4x"},"classes":[]},{"id":1430,"url":"https:\/\/scienceblog.com\/horizon\/1430\/coronavirus-accelerates-drive-to-share-health-data-across-borders\/","url_meta":{"origin":1422,"position":2},"title":"Coronavirus accelerates drive to share health data across borders","author":"Richard Gray","date":"September 10, 2020","format":false,"excerpt":"Allowing health data to flow more freely between countries in Europe could aid the fight against coronavirus while also help the region be better prepared for future pandemics, but privacy and technical considerations need to be tackled sooner rather than later, say experts. In the midst of a global pandemic,\u2026","rel":"","context":"In "Health"","block_context":{"text":"Health","link":"https:\/\/scienceblog.com\/horizon\/category\/health\/"},"img":{"alt_text":"Data-sharing can help with the next health crisis but that means privacy and technical issues need to now be addressed, say experts. 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Prof.\u2026","rel":"","context":"In "Health"","block_context":{"text":"Health","link":"https:\/\/scienceblog.com\/horizon\/category\/health\/"},"img":{"alt_text":"n the future, smartphone apps may help diagnose disease by analysing someone's voice, cough and breathing. 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