{"id":19814,"date":"2019-01-17T05:00:00","date_gmt":"2019-01-17T12:00:00","guid":{"rendered":"https:\/\/www.salesforce.com\/?p=19814"},"modified":"2020-09-18T00:36:06","modified_gmt":"2020-09-18T00:36:06","slug":"ai-isnt-dangerous-but-human-bias-is","status":"publish","type":"post","link":"https:\/\/www.salesforce.com\/news\/stories\/ai-isnt-dangerous-but-human-bias-is\/","title":{"rendered":"AI Isn\u2019t Dangerous, but Human Bias Is"},"content":{"rendered":"<p>AI is rapidly becoming part of the fabric of our daily lives as it moves out of academia and research labs and into the real world.<\/p><p>I&#8217;m not concerned about AI superintelligence \u201cgoing rogue\u201d and challenging the survival of the human race \u2013 that&#8217;s science fiction and unsupported by any scientific research today. But I do believe we have to think about any unintended consequences of using this technology.<\/p><p>Kathy Baxter, Salesforce\u2019s ethical AI Practice architect, put the problem well. Pointing out that AI is not sentient but merely a tool and therefore morally neutral, she reminded us that its use depends on the criteria we humans apply to its development. \u201cWhile AI has the potential to do tremendous good, it can also have the potential for unknowingly harming individuals.\u201d<\/p><p><b>Unmanaged AI is a mirror for human bias<\/b><\/p><p>One way that AI can cause harm is when algorithms reflect our human biases in the datasets that organizations collect. The effects of these biases can compound in the AI era, as the algorithms themselves continue to \u201clearn\u201d from the data.<\/p><p>Let\u2019s imagine, for example, that a bank wants to predict whether it should give someone a loan. Let\u2019s also imagine that in the past, this particular bank hasn\u2019t given as many loans to women or people from certain minorities.<\/p><p>These features will be present in that bank\u2019s dataset \u2013 which could make it easier for an AI algorithm to draw the conclusion that women or people from minority groups are more likely to be credit risks and should therefore not be given loans.<\/p><p>In other words, the lack of data on loans to certain people in the past could have an impact on how the bank\u2019s AI program will treat their loan applications in the future. The system could pick up a bias and amplify it \u2013 or at the very least, perpetuate it.<\/p><p>Of course, AI algorithms could also be gamed by explicit prejudice, where someone curates data in an AI system in a way that excludes, say, women of color being considered for loans.<\/p><p>Either way, AI is only as good as the data \u2013 specifically, the \u201ctraining data\u201d \u2013 we give it. All this means it\u2019s vital for anyone involved with training AI programs to consider just how representative any training data they use actually is.<\/p><p>As Kathy put it, by simply plucking data from the internet to train AI programmes there\u2019s a good chance that we will \u201cmagnify the stereotypes, the biases, and the false information that already exist\u201d.<\/p><p><b>Embedding diversity and removing bias<\/b><\/p><p>So how do we manage the threats of biased AI? It starts with humans proactively identifying and managing any such bias \u2013 which includes training AI systems to identify it.<\/p><p>AI bias isn\u2019t the result of the technology being flawed. Algorithms don\u2019t become biased on their own \u2013 they learn that from us. So we have to take responsibility for helping to avoid any negative effects spawned from the AI systems that we\u2019ve created. For example, a bank could exclude gender and race from its dataset when setting up an AI system to score the viability of applicants for loans.<\/p><p>At the same time, companies also need to be aware of hidden biases. Say our imaginary bank removed gender and race from its AI model, but left in zip codes \u2013 often a proxy for race in the US. Such a move could still lead to the bank\u2019s algorithms producing biased predictions that discriminated against historically underprivileged neighborhoods.<\/p><p>To prevent AI systems from allowing bias to influence recommendations in any way at all, we need to rely on human understanding, and not just technology.<\/p><p>Core to this is making sure that the people who are building AI systems reflect a diverse set of perspectives. A homogenous group may only hear opinions that match their own \u2013 they\u2019re limited to what they know, in other words, and are less likely to spot where bias might be present.<\/p><p>With such guidance, organizations can curate bias out of their training data to mitigate negative effects. This can also help to make AI systems more transparent and less inscrutable, which in turn makes it easier for companies to check for any errors that might be taking place.<\/p><p><b>Taking ethical AI to the next level<\/b><\/p><p>The good news is that I\u2019m seeing efforts to tackle bias currently moving in tandem with a broader global debate about AI ethics. That\u2019s being led by groups such as the <a href=\"https:\/\/www.partnershiponai.org\/\">Partnership on AI<\/a>, a consortium made up of technology and education leaders, including Salesforce, from varied fields.<\/p><p>These groups are joining forces to focus on the responsible use and development of AI technologies. They\u2019re doing so by advancing the understanding of AI, establishing best practice, and harnessing AI to contribute to solutions for some of humanity\u2019s most challenging problems.<\/p><p>Taking these debates to a wider stage is important because any process that involves the responsible use of AI should not be seen as simply another set of tasks. Instead, we need to fundamentally change people\u2019s thinking and behavior, and we need to do it now.<\/p><p>To put it another way, if AI does hold a mirror to humanity, it\u2019s up to us to be accountable, now, and ensure what\u2019s reflected shows our best face.<\/p><p><i>Editor&#8217;s Note: This story was first published on the <a href=\"https:\/\/www.weforum.org\/agenda\/2019\/01\/ai-isn-t-dangerous-but-human-bias-is\/\">World Economic Forum site<\/a> in conjunction with the <a href=\"https:\/\/www.weforum.org\/events\/world-economic-forum-annual-meeting\">2019 World Economic Forum Annual Meeting<\/a> in Davos, Switzerland.<\/i><\/p>","protected":false},"excerpt":{"rendered":"<p>AI is rapidly becoming part of the fabric of our daily lives as it moves out of academia and research labs and into the real world. I&#8217;m not concerned about AI superintelligence \u201cgoing rogue\u201d and challenging the survival of the human race \u2013 that&#8217;s science fiction and unsupported by any scientific research today. But I [&hellip;]<\/p>\n","protected":false},"author":50,"featured_media":19463,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"sf_subhead":"","sf_i18n_disclaimer":false,"_jetpack_memberships_contains_paid_content":false,"alternateThumbnailId":0,"sf_product_cta_id":0,"footnotes":""},"categories":[1],"tags":[],"sf_content_type":[1730],"sf_theme":[1916],"sf_topic":[1737,1763],"sf_product":[],"sf_industry":[],"sf_role":[],"sf_multimedia_asset":[],"sf_location":[1724],"sf_collection":[],"sf_visibility":[],"coauthors":[2008],"class_list":["post-19814","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","sf_content_type-feature-stories","sf_theme-artificial-intelligence","sf_topic-artificial-intelligence","sf_topic-innovation","sf_location-amer"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.2 (Yoast SEO v27.2) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>AI Isn\u2019t Dangerous, but Human Bias Is - 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