{"id":16204,"date":"2020-02-08T18:00:29","date_gmt":"2020-02-08T12:30:29","guid":{"rendered":"https:\/\/www.the-next-tech.com\/?p=16204"},"modified":"2020-02-10T10:53:03","modified_gmt":"2020-02-10T05:23:03","slug":"importance-of-data-diversity-to-avoid-bias","status":"publish","type":"post","link":"https:\/\/www.the-next-tech.com\/artificial-intelligence\/importance-of-data-diversity-to-avoid-bias\/","title":{"rendered":"Importance of Data Diversity to Avoid Bias"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">The world is connected today in more ways than it ever has been before, as billions of objects are now capable of connecting to the internet or interfacing with devices that are already online.\u00a0 The new \u201cInternet of Everything\u201d generates a deluge of data, which is increasingly directed to the cloud for processing and storage. Meanwhile, <\/span><a href=\"https:\/\/en.wikipedia.org\/wiki\/Artificial_intelligence\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">Artificial intelligence<\/span><\/a> <span style=\"font-weight: 400;\">is increasingly utilized to analyze and derive value from these enormous stores of data.\u00a0 In industries such as <\/span><span style=\"font-weight: 400;\">healthcare<\/span><span style=\"font-weight: 400;\">, <\/span><span style=\"font-weight: 400;\">transportation<\/span><span style=\"font-weight: 400;\">, <\/span><span style=\"font-weight: 400;\">industrial manufacturing<\/span><span style=\"font-weight: 400;\">, and <\/span><span style=\"font-weight: 400;\">financial services<\/span><span style=\"font-weight: 400;\">, AI algorithms are now being applied to increasingly difficult tasks, including critical decision-making processes.\u00a0<\/span><\/p>\n<blockquote><p><strong>What differentiates human from machine is the quality of judgement, creativity, and critical thinking.\u00a0 Humans still have the edge, but intelligent machines are slowing progressing in their ability to replicate the human decision-making process. Deep learning algorithms utilize artificial neural networks inspired by the human brain, performing a task repeatedly with small variations to find an optimal outcome.\u00a0\u00a0<\/strong><\/p><\/blockquote>\n<p><span style=\"font-weight: 400;\">The key to success in<a href=\"https:\/\/www.the-next-tech.com\/machine-learning\/what-is-the-difference-between-deep-learning-machine-learning-and-ai\/\"> Machine Learning and ultimately Artificial Intelligence<\/a> is data.\u00a0 Copious amounts of data along with rapidly advancing computing power allow machines to solve increasingly complex problems. Data not only needs to be plentiful but it also needs to be clean, representative, and balanced.\u00a0 If training data is not wholly representative of the diversity of a general population, then the results will undoubtedly be subject to bias. Such biases, whether intended or unintended, can manifest in subtle ways or via colossal and public failures such as the recent examples of age, gender and racial bias found in the ML offerings of some of the world\u2019s largest software companies.<\/span><br \/>\n<!-- Home page 728x90 --><br \/>\n<ins class=\"adsbygoogle\" style=\"display: inline-block; width: 728px; height: 90px;\" data-ad-client=\"ca-pub-9864771813712812\" data-ad-slot=\"3152971286\"><\/ins><br \/>\n<script>\n(adsbygoogle = window.adsbygoogle || []).push({});\n<\/script><br \/>\n<span style=\"font-weight: 400;\">The issue of bias is well documented in sociology, psychology, and other disciplines.\u00a0 Our society has implemented many different safeguards to ensure that bias, and its more offensive derivatives prejudice and discrimination, are kept in check across situations as varied as employment, creditworthiness, education, and social club membership.\u00a0 Because algorithms are increasingly being used to guide important decisions that affect large groups of people, it is critical that similar safeguards are enacted to identify and correct issues of bias in <a href=\"https:\/\/www.the-next-tech.com\/machine-learning\/machine-learning-and-ai-the-mystery-isnt-solved-yet\/\">machine learning and AI<\/a>. This bias is often unintended and can also go unnoticed for a long time, so it is important to carefully evaluate the prediction results from a model to look specifically for instances of bias.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Machine learning models are entirely reliant on the underlying data that they were trained on. If this training data is biased, limited, unbalanced, or flawed in some fashion then the model will inevitably end up producing biased outputs. Data Scientists must exercise care and caution in their data collection and data labeling phases. Data should be balanced and diverse and ideally cover corner cases.\u00a0 If related to populations of humans in some way, such as in face recognition or sentiment analysis, it is important to achieve balanced and representative training data from a global pool of subjects if the model will potentially be applied to a global pool of actual data.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">AI<\/span> <span style=\"font-weight: 400;\">provides a comprehensive solution for your data collection and annotation needs. We often assist clients seeking to improve diversity in training data by offering a spectrum of regions from which data can be collected.\u00a0 We utilize our global network of partners and affiliates to collect samples from Asia, Africa, Europe, and the Middle East. Meanwhile our proprietary annotation platform ensures highly accurate and cost-efficient data labeling in the cloud or on premises. With a focus on accuracy and effectiveness, AI is committed to providing world-class annotation solutions across industry sectors.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The world is connected today in more ways than it ever has been before, as billions of objects are now<\/p>\n","protected":false},"author":146,"featured_media":16338,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[36],"tags":[741,877,879,719,594,137,164,391,392,145],"_links":{"self":[{"href":"https:\/\/www.the-next-tech.com\/rest\/wp\/v2\/posts\/16204"}],"collection":[{"href":"https:\/\/www.the-next-tech.com\/rest\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.the-next-tech.com\/rest\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.the-next-tech.com\/rest\/wp\/v2\/users\/146"}],"replies":[{"embeddable":true,"href":"https:\/\/www.the-next-tech.com\/rest\/wp\/v2\/comments?post=16204"}],"version-history":[{"count":2,"href":"https:\/\/www.the-next-tech.com\/rest\/wp\/v2\/posts\/16204\/revisions"}],"predecessor-version":[{"id":16425,"href":"https:\/\/www.the-next-tech.com\/rest\/wp\/v2\/posts\/16204\/revisions\/16425"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.the-next-tech.com\/rest\/wp\/v2\/media\/16338"}],"wp:attachment":[{"href":"https:\/\/www.the-next-tech.com\/rest\/wp\/v2\/media?parent=16204"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.the-next-tech.com\/rest\/wp\/v2\/categories?post=16204"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.the-next-tech.com\/rest\/wp\/v2\/tags?post=16204"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}