{"id":569,"date":"2020-06-10T15:19:43","date_gmt":"2020-06-10T15:19:43","guid":{"rendered":"http:\/\/sigai.acm.org\/aimatters\/blog\/?p=569"},"modified":"2020-06-10T15:19:43","modified_gmt":"2020-06-10T15:19:43","slug":"ai-and-facial-recognition","status":"publish","type":"post","link":"https:\/\/sigai.acm.org\/main\/2020\/06\/10\/ai-and-facial-recognition\/","title":{"rendered":"AI and Facial Recognition"},"content":{"rendered":"\n

AI in Congress<\/strong><\/p>\n\n\n\n

Politico reports<\/a> on two separate bills introduced Thursday, June 2. (See the section entitled \u201cArtificial Intelligence: Let\u2019s Do the Thing\u201d.)<\/p>\n\n\n\n

The National AI Research Resource Task Force Act. \u201cThe bipartisan, bicameral bill introduced by Reps. Anna Eshoo, (D-Calif.), Anthony Gonzalez (R-Ohio), and Mikie Sherrill (D-N.J.), along with companion legislation by Sens. Rob Portman (R-Ohio) and Martin Heinrich(D-N.M.), would form a committee to figure out how to launch and best use a national AI research cloud<\/em>. Public and private researchers and developers from across the country would share this cloud to combine their data, computing power and other resources on AI. The panel would include experts from government, academia and the private sector.\u201d<\/p>\n\n\n\n

The Advancing Artificial Intelligence Research Act. \u201cThe bipartisan bill\nintroduced by Senate Commerce Chairman Roger Wicker (R-Miss.), Sen. Cory\nGardner (R-Colo.) and Gary Peters (D-Mich.), a founding member\nof the Senate AI Caucus, would create a program to accelerate research and\ndevelopment of guidance around AI at the National Institute of Standards\nand Technology. It would also create at least a half-dozen AI research\ninstitutes<\/em> to examine the benefits and challenges of the emerging\ntechnology and how it can be deployed; provide funding to universities and\nnonprofits researching AI; and launch a pilot at the National Science Foundation\nfor AI research grants.\u201d<\/p>\n\n\n\n

Concerns About Facial Recognition (FR): Discrimination, Privacy,\nand Democratic Freedom<\/strong><\/p>\n\n\n\n

While including ethical and\nmoral issues, a broader list of issues is concerning to citizens and policymakers\nabout face recognition technology and AI. Areas of concerns include accuracy; surveillance;\ndata storage, permissions, and access; discrimination, fairness, and bias; privacy\nand video recording without consent; democratic freedoms, including right to\nchoose, gather, and speak; and abuse of technology such as non-intended uses,\nhacking, and deep fakes. Used responsibly and ethically, face recognition can\nbe valuable for finding missing people, responsible policing and law\nenforcement, medical uses, healthcare, virus tracking, legal system and court\nuses, and advertising. Various guidelines by organizations such as the AMA<\/a>\nand legislation like S.3284 – Ethical Use of Facial\nRecognition Act<\/a> are being developed to encourage the proper use of AI and face\nrecognition.<\/p>\n\n\n\n

Some of the above issues do specifically\nrequire ethical analysis as in the following by Yaroslav\nKuflinski<\/a>:<\/p>\n\n\n\n

Accuracy \u2014 FR systems\nnaturally discriminate against non-whites, women, and children, presenting\nerrors of up to 35% for non-white women.<\/p>\n\n\n\n

Surveillance issues \u2014 concerns\nabout \u201cbig brother\u201d watching society.<\/p>\n\n\n\n

Data storage \u2014 use of images\nfor future purposes stored alongside genuine criminals.<\/p>\n\n\n\n

Finding missing people \u2014 breaches\nof the right to a private life. <\/p>\n\n\n\n

Advertising \u2014 invasion of\nprivacy by displaying information and preferences that a buyer would prefer to\nkeep secret.<\/p>\n\n\n\n

Studies of commercial systems are increasingly\navailable, for example an analysis<\/a>\nof Amazon Rekognition.<\/p>\n\n\n\n

Biases deriving from sources\nof unfairness and discrimination in machine learning have been identified in\ntwo areas: the data and the algorithms.  Biases in data skew what is\nlearned in machine learning methods, and flaws in algorithms can lead to unfair\ndecisions even when the data is unbiased. Intentional or unintentional biases\ncan exist in the data used to train FR systems.<\/p>\n\n\n\n

New human-centered design approaches\nseek to provide intentional system development steps and processes in\ncollecting data and creating high quality databases, including the elimination\nof naturally occurring bias reflected in data about real people.<\/p>\n\n\n\n

Bias That Pertains Especially to Facial Recognition<\/strong> (Mehrabi, et al.<\/a> and Barocas, et al.<\/a>)<\/p>\n\n\n\n

Direct Discrimination: \u201cDirect discrimination happens\nwhen protected attributes of individuals explicitly result in non-favorable\noutcomes toward them\u201d.  Some traits like race, color, national origin,\nreligion, sex, family status, disability, exercised rights under CCPA , marital\nstatus, receipt of public assistance, and age are identified as sensitive\nattributes or protected attributes in the machine learning world.                        <\/p>\n\n\n\n

Indirect Discrimination: Even if sensitive or protected\nattributes are not used against an individual, indirect discrimination can still\nhappen. For example, residential zip code is not categorized as a protected\nattribute, but from the zip code one might infer race, which is a protected\nattribute. So, \u201cprotected groups or individuals still can get treated unjustly\nas a result of implicit effects from their protected attributes\u201d.<\/p>\n\n\n\n

Systemic Discrimination: \u201cpolicies, customs, or behaviors that are a part of the culture or structure of an organization that may perpetuate discrimination against certain subgroups of the population\u201d.<\/p>\n\n\n\n

Statistical Discrimination: In law enforcement, racial\nprofiling is an example of statistical discrimination. In this case, minority\ndrivers are pulled over more than compared to white drivers \u2014 \u201cstatistical\ndiscrimination is a phenomenon where decision-makers use average group\nstatistics to judge an individual belonging to that group.\u201d<\/p>\n\n\n\n

Explainable Discrimination: In some cases, discrimination\ncan be explained using attributes like working hours and education, which is\nlegal and acceptable. In \u201cthe UCI Adult dataset [6], a widely-used dataset in\nthe fairness domain, males on average have a higher annual income than females;\nhowever, this is because on average females work fewer hours than males per\nweek. Work hours per week is an attribute that can be used to explain low\nincome. If we make decisions without considering working hours such that males\nand females end up averaging the same income, we could lead to reverse\ndiscrimination since we would cause male employees to get lower salary than\nfemales.                              <\/p>\n\n\n\n

Unexplainable Discrimination: This type of discrimination is\nnot legal as explainable discrimination because \u201cthe discrimination toward a\ngroup is unjustified\u201d.<\/p>\n\n\n\n

How to Discuss\nFacial Recognition<\/strong><\/p>\n\n\n\n

Recent controversies about FR mix technology issues with ethical\nimperatives and ignore that people can disagree on which are the \u201ccorrect\u201d ethical\nprinciples. A recent ACM tweet on FR and face masks was interpreted in\ndifferent ways and ACM issued an\nofficial clarification<\/a>. A question that emerges is if AI and other technologies\nshould be, and can be, banned rather than controlled and regulated. <\/p>\n\n\n\n

In early June, 2020, IBM CEO Arvind Krishna said in a letter to\nCongress that IBM is exiting the facial recognition business and asking\nfor reforms to combat racism: “IBM no longer offers general purpose IBM\nfacial recognition or analysis software. IBM firmly opposes and will not\ncondone uses of any technology, including facial recognition technology offered\nby other vendors, for mass surveillance, racial profiling, violations of basic\nhuman rights and freedoms, or any purpose which is not consistent with our\nvalues and Principles of Trust and Transparency,” Krishna said in his\nletter to members of congress<\/a>, “We believe now is the time to begin a national\ndialogue on whether and how facial recognition technology should be employed by\ndomestic law enforcement agencies.”<\/p>\n\n\n\n

The guest co-author of this series of blog posts on AI and\nbias is Farhana Faruqe, doctoral student in the George Washington University\nHuman-Technology Collaboration program.<\/p>\n","protected":false},"excerpt":{"rendered":"

AI in Congress Politico reports on two separate bills introduced Thursday, June 2. (See the section entitled \u201cArtificial Intelligence: Let\u2019s Do the Thing\u201d.) The National AI Research Resource Task Force Act. \u201cThe bipartisan, bicameral bill introduced by Reps. Anna Eshoo, (D-Calif.), Anthony Gonzalez (R-Ohio), and Mikie Sherrill (D-N.J.), along with companion legislation by Sens. Rob Portman (R-Ohio) and Martin Heinrich(D-N.M.), would form a committee […]<\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","inline_featured_image":false,"footnotes":""},"categories":[5,12],"tags":[],"yoast_head":"\nAI and Facial Recognition - ACM SIGAI<\/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:\/\/sigai.acm.org\/main\/2020\/06\/10\/ai-and-facial-recognition\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI and Facial Recognition - ACM SIGAI\" \/>\n<meta property=\"og:description\" content=\"AI in Congress Politico reports on two separate bills introduced Thursday, June 2. (See the section entitled \u201cArtificial Intelligence: Let\u2019s Do the Thing\u201d.) The National AI Research Resource Task Force Act. \u201cThe bipartisan, bicameral bill introduced by Reps. Anna Eshoo, (D-Calif.), Anthony Gonzalez (R-Ohio), and Mikie Sherrill (D-N.J.), along with companion legislation by Sens. Rob Portman (R-Ohio) and Martin Heinrich(D-N.M.), would form a committee […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sigai.acm.org\/main\/2020\/06\/10\/ai-and-facial-recognition\/\" \/>\n<meta property=\"og:site_name\" content=\"ACM SIGAI\" \/>\n<meta property=\"article:published_time\" content=\"2020-06-10T15:19:43+00:00\" \/>\n<meta name=\"author\" content=\"Larry Medsker\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Larry Medsker\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/sigai.acm.org\/main\/2020\/06\/10\/ai-and-facial-recognition\/\",\"url\":\"https:\/\/sigai.acm.org\/main\/2020\/06\/10\/ai-and-facial-recognition\/\",\"name\":\"AI and Facial Recognition - ACM SIGAI\",\"isPartOf\":{\"@id\":\"https:\/\/sigai.acm.org\/main\/#website\"},\"datePublished\":\"2020-06-10T15:19:43+00:00\",\"author\":{\"@id\":\"https:\/\/sigai.acm.org\/main\/#\/schema\/person\/5097a3e1c76f2c205fe0f5ebb9b51fdb\"},\"breadcrumb\":{\"@id\":\"https:\/\/sigai.acm.org\/main\/2020\/06\/10\/ai-and-facial-recognition\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/sigai.acm.org\/main\/2020\/06\/10\/ai-and-facial-recognition\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/sigai.acm.org\/main\/2020\/06\/10\/ai-and-facial-recognition\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/sigai.acm.org\/main\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"AI and Facial Recognition\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/sigai.acm.org\/main\/#website\",\"url\":\"https:\/\/sigai.acm.org\/main\/\",\"name\":\"ACM SIGAI\",\"description\":\"ACM Special Interest Group on Artificial Intelligence\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/sigai.acm.org\/main\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/sigai.acm.org\/main\/#\/schema\/person\/5097a3e1c76f2c205fe0f5ebb9b51fdb\",\"name\":\"Larry Medsker\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/sigai.acm.org\/main\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/a175bde07d4c8846a16bc64afa6e97f1?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/a175bde07d4c8846a16bc64afa6e97f1?s=96&d=mm&r=g\",\"caption\":\"Larry Medsker\"},\"url\":\"https:\/\/sigai.acm.org\/main\/author\/larrym\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"AI and Facial Recognition - ACM SIGAI","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/sigai.acm.org\/main\/2020\/06\/10\/ai-and-facial-recognition\/","og_locale":"en_US","og_type":"article","og_title":"AI and Facial Recognition - ACM SIGAI","og_description":"AI in Congress Politico reports on two separate bills introduced Thursday, June 2. (See the section entitled \u201cArtificial Intelligence: Let\u2019s Do the Thing\u201d.) The National AI Research Resource Task Force Act. \u201cThe bipartisan, bicameral bill introduced by Reps. Anna Eshoo, (D-Calif.), Anthony Gonzalez (R-Ohio), and Mikie Sherrill (D-N.J.), along with companion legislation by Sens. Rob Portman (R-Ohio) and Martin Heinrich(D-N.M.), would form a committee […]","og_url":"https:\/\/sigai.acm.org\/main\/2020\/06\/10\/ai-and-facial-recognition\/","og_site_name":"ACM SIGAI","article_published_time":"2020-06-10T15:19:43+00:00","author":"Larry Medsker","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Larry Medsker","Est. reading time":"6 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/sigai.acm.org\/main\/2020\/06\/10\/ai-and-facial-recognition\/","url":"https:\/\/sigai.acm.org\/main\/2020\/06\/10\/ai-and-facial-recognition\/","name":"AI and Facial Recognition - ACM SIGAI","isPartOf":{"@id":"https:\/\/sigai.acm.org\/main\/#website"},"datePublished":"2020-06-10T15:19:43+00:00","author":{"@id":"https:\/\/sigai.acm.org\/main\/#\/schema\/person\/5097a3e1c76f2c205fe0f5ebb9b51fdb"},"breadcrumb":{"@id":"https:\/\/sigai.acm.org\/main\/2020\/06\/10\/ai-and-facial-recognition\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/sigai.acm.org\/main\/2020\/06\/10\/ai-and-facial-recognition\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/sigai.acm.org\/main\/2020\/06\/10\/ai-and-facial-recognition\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/sigai.acm.org\/main\/"},{"@type":"ListItem","position":2,"name":"AI and Facial Recognition"}]},{"@type":"WebSite","@id":"https:\/\/sigai.acm.org\/main\/#website","url":"https:\/\/sigai.acm.org\/main\/","name":"ACM SIGAI","description":"ACM Special Interest Group on Artificial Intelligence","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/sigai.acm.org\/main\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Person","@id":"https:\/\/sigai.acm.org\/main\/#\/schema\/person\/5097a3e1c76f2c205fe0f5ebb9b51fdb","name":"Larry Medsker","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/sigai.acm.org\/main\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/a175bde07d4c8846a16bc64afa6e97f1?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/a175bde07d4c8846a16bc64afa6e97f1?s=96&d=mm&r=g","caption":"Larry Medsker"},"url":"https:\/\/sigai.acm.org\/main\/author\/larrym\/"}]}},"_links":{"self":[{"href":"https:\/\/sigai.acm.org\/main\/wp-json\/wp\/v2\/posts\/569"}],"collection":[{"href":"https:\/\/sigai.acm.org\/main\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sigai.acm.org\/main\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sigai.acm.org\/main\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/sigai.acm.org\/main\/wp-json\/wp\/v2\/comments?post=569"}],"version-history":[{"count":0,"href":"https:\/\/sigai.acm.org\/main\/wp-json\/wp\/v2\/posts\/569\/revisions"}],"wp:attachment":[{"href":"https:\/\/sigai.acm.org\/main\/wp-json\/wp\/v2\/media?parent=569"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sigai.acm.org\/main\/wp-json\/wp\/v2\/categories?post=569"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sigai.acm.org\/main\/wp-json\/wp\/v2\/tags?post=569"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}