Many studies regarding\nthe face detection, can categorize people biasedly according to their\ncharacteristics such as skin color and gender. MIT is working to improve this\nproblem with a better algorithm, rather than solving it manually.<\/p>\n\n\n\n
BIAS-FREE FACE\nDETECTION.<\/strong><\/strong><\/p>\n\n\n\n MIT has been able to achieve\nreducing the categorical bias problems by 60 percent, with its new algorithm developed\non the face detection. However, efficient results can be achieved in a shorter\ntime on a larger data set. These improvements became important because the face\ndetection systems became widespread and its reliability ratio is increased in\nmany organizations and companies. Failure of solving these problems can also\ncause major problems such as difficulties in using the face detection products,\nproducing false results for people, and making wrong decisions.<\/p>\n\n\n\n Artificial intelligence,\ndeveloped by MIT’s CSAIL researchers, is improving with bias-free educational\nmaterials.<\/p>\n\n\n\n Face detection technology\nhas entered our lives in a short period of time, however, it is claimed that the systems approach with a\nracial bias in some cases. The face detection system developed by Amazon, which\nwas started to use by the US Immigration and Customs Enforcement in recent\nmonths, has caused controversies. Senate members are questioning how Amazon is\ntesting the system and the possibility of creating a racial bias. The\nimprovement algorithm, that MIT is working on, could be the solution movement for\nthis technology, which will become more widespread.<\/p>\n\n\n\n Source:<\/strong> https:\/\/www.engadget.com\/2019\/01\/27\/mit-automatically-reduces-racist-biases-in-face-detection\/<\/p>\n","protected":false},"excerpt":{"rendered":" Many studies regarding the face detection, can categorize people biasedly according to their characteristics such as skin color and gender. MIT is working to improve this problem with a better algorithm, rather than solving it manually. BIAS-FREE FACE DETECTION. MIT has been able to achieve reducing the categorical bias problems by 60 percent, with its …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[151],"tags":[],"class_list":["post-6130","post","type-post","status-publish","format-standard","","category-sector-news"],"_links":{"self":[{"href":"https:\/\/www.isaffuari.com\/en\/wp-json\/wp\/v2\/posts\/6130","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.isaffuari.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.isaffuari.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.isaffuari.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.isaffuari.com\/en\/wp-json\/wp\/v2\/comments?post=6130"}],"version-history":[{"count":0,"href":"https:\/\/www.isaffuari.com\/en\/wp-json\/wp\/v2\/posts\/6130\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.isaffuari.com\/en\/wp-json\/wp\/v2\/media?parent=6130"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.isaffuari.com\/en\/wp-json\/wp\/v2\/categories?post=6130"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.isaffuari.com\/en\/wp-json\/wp\/v2\/tags?post=6130"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}