{"id":4016,"date":"2020-12-02T02:12:38","date_gmt":"2020-12-02T02:12:38","guid":{"rendered":"https:\/\/techclot.com\/index.php\/2020\/12\/02\/why-black-box-ai-problem-is-bad-for-business\/"},"modified":"2020-12-02T02:12:38","modified_gmt":"2020-12-02T02:12:38","slug":"why-black-box-ai-problem-is-bad-for-business","status":"publish","type":"post","link":"https:\/\/techclot.com\/index.php\/2020\/12\/02\/why-black-box-ai-problem-is-bad-for-business\/","title":{"rendered":"Why black box AI problem is bad for business"},"content":{"rendered":"

Why black box AI problem is bad for business<\/a><\/p>\n

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\n Artificial intelligence is a powerful technology, but if we\u2019re not able to show its workings, gaining acceptance will remain a challenge. <\/div>\n

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\n 1 December 2020\n <\/div>\n<\/p><\/div>\n<\/p><\/div>\n

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\n Black Box. Source: Shutterstock <\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n

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Deep learning algorithms take millions of data points as inputs, correlating specific features to produce an output.<\/p>\n

While humans are involved in the initial management of data, such as data labeling<\/a>, once fed into a system the process is largely self-directed.<\/p>\n

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Even for the data scientists and programs involved in the model\u2019s development, it can be difficult to interpret and subsequently explain how a process has led to a specific output.<\/p>\n

This is a complex issue earning the label \u2018black box AI\u2019 \u2014 and it is becoming a greater problem as artificial intelligence (AI) and machine learning plays a bigger role in our day-to-day and working lives.<\/p>\n

When the workings of software used for important operations and processes within a business cannot be easily viewed or understood, errors and bias can go unnoticed and snowball into much bigger, potentially irreparable, problems.<\/p>\n

Put in the context of AI\u2019s use today, whether that\u2019s the shortlisting and hiring of candidates<\/a> for roles or loan decisions by banks, it\u2019s clear to see why black box AI poses a problem.<\/p>\n

Last year, the Information Commissioner\u2019s Office (ICO) put forward regulations<\/a> that would require businesses and other organizations are required to explain decisions made by artificial intelligence.<\/p>\n

The group urged organizations using AI to produce documentation explaining the mechanics of models in use, as the use of the technology becomes rapidly widespread.<\/p>\n

Organizations that cannot explain how an automated decision was made could even be in breach of the General Data Protection Regulation (GDPR), putting them at risk of potentially multi-million-dollar fines, depending on annual revenue.<\/p>\n

Workers\u2019 rights<\/h3>\n

But these guidelines haven\u2019t necessarily done enough \u2014 to contest a decision made by an algorithm, an individual or organization would first have to know AI was behind the decision made. But disclosure is lacking.<\/p>\n

In the UK, trade unions are now forming an AI task-force to lobby employers and regulators to increase transparency around where the technology is being deployed, and to offer workers recourse if they believe they are being discriminated against.<\/p>\n

The initiative is being driven by the Trades Union Congress (TUC), an umbrella group for unions representing more than 5.5 million people.<\/p>\n

\u201cWe realized that essentially there\u2019s a revolution taking place in the world of work in terms of the use of AI to manage people,\u201d Mary Towers, an employment rights policy officer at the TUC who\u2019s leading the effort, told Bloomberg<\/em>.<\/p>\n

\u201cWhat we\u2019re really talking about is the use of AI to make decisions that really impact significantly on people\u2019s job opportunities and the nature and fabric of their working lives.\u201d<\/p>\n

Employees speaking to the TUC said they are encountering AI in job applications and interviews, in shift and vacation management decisions, and performance analysis \u2014 areas where AI errors could result in unfair outcomes or discrimination.<\/p>\n

The TUC said that a lack of transparency is compounding the problem. Just 14% of employees thought they would know if AI had made an automated decision about a job application, according to the UK\u2019s Centre for Data Ethics.<\/p>\n

It\u2019s not just employees that are lacking trust in AI. Concerns over the ability to trust AI-enabled decisions could hold back the adoption of the technology more broadly, limiting or delaying its further advance and wider benefits.<\/p>\n

The solution, according to the TUC, starts with unions finding ways to negotiate increasing transparency on where AI is being used, and control over the data that\u2019s collected and used by algorithms.<\/p>\n

AI is a powerful tool, but if we\u2019re not able to show its workings, gaining acceptance will be difficult.<\/p>\n

Last year, Google launched Explainable AI on its cloud platform, a set of tools and frameworks for developing machine learning<\/a> models that allow developers to \u201cvisually investigate model behavior\u201d and \u201cdetect and resolve bias, drift, and other gaps\u201d in machine learning models.<\/p>\n

\u201cMachine learning models can identify intricate correlations between enormous numbers of data points,\u201d said the firm\u2019s Cloud AI Director of Strategy, Tracey Frey in a blog post<\/a>; \u201cWhile this capability allows AI models to reach incredible accuracy, inspecting the structure or weights of a model often tells you little about a model\u2019s behavior.<\/p>\n

\u201cThis means that for some decision-makers, particularly those in industries where confidence is critical, the benefits of AI can be out of reach without interpretability.\u201d<\/p>\n

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1 December 2020<\/p>\n<\/p><\/div>\n

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1 December 2020<\/p>\n<\/p><\/div>\n

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30 November 2020<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n

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Published at Tue, 01 Dec 2020 09:31:05 +0000<\/p>\n

Why black box AI problem is bad for business<\/a><\/p>\n

<\/p>\n

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<\/p>\n

<\/p>\n

\n

<\/p>\n

<\/p>\n

<\/p>\n

\n Artificial intelligence is a powerful technology, but if we\u2019re not able to show its workings, gaining acceptance will remain a challenge. <\/div>\n

<\/p>\n

\n 1 December 2020\n <\/div>\n<\/p><\/div>\n<\/p><\/div>\n

<\/p>\n

\n <\/p>\n
\n \"A<\/div>\n

<\/p>\n

\n <\/p>\n
\n
\n
\"Mark<\/div>\n<\/p><\/div>\n<\/p><\/div>\n

<\/p>\n

\n

\n Black Box. Source: Shutterstock <\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n

<\/p>\n

\n <\/p>\n

<\/p>\n

\n

<\/p>\n

Deep learning algorithms take millions of data points as inputs, correlating specific features to produce an output.<\/p>\n

While humans are involved in the initial management of data, such as data labeling<\/a>, once fed into a system the process is largely self-directed.<\/p>\n

\n
\n
<\/div>\n<\/div>\n<\/div>\n

Even for the data scientists and programs involved in the model\u2019s development, it can be difficult to interpret and subsequently explain how a process has led to a specific output.<\/p>\n

This is a complex issue earning the label \u2018black box AI\u2019 \u2014 and it is becoming a greater problem as artificial intelligence (AI) and machine learning plays a bigger role in our day-to-day and working lives.<\/p>\n

When the workings of software used for important operations and processes within a business cannot be easily viewed or understood, errors and bias can go unnoticed and snowball into much bigger, potentially irreparable, problems.<\/p>\n

Put in the context of AI\u2019s use today, whether that\u2019s the shortlisting and hiring of candidates<\/a> for roles or loan decisions by banks, it\u2019s clear to see why black box AI poses a problem.<\/p>\n

Last year, the Information Commissioner\u2019s Office (ICO) put forward regulations<\/a> that would require businesses and other organizations are required to explain decisions made by artificial intelligence.<\/p>\n

The group urged organizations using AI to produce documentation explaining the mechanics of models in use, as the use of the technology becomes rapidly widespread.<\/p>\n

Organizations that cannot explain how an automated decision was made could even be in breach of the General Data Protection Regulation (GDPR), putting them at risk of potentially multi-million-dollar fines, depending on annual revenue.<\/p>\n

Workers\u2019 rights<\/h3>\n

But these guidelines haven\u2019t necessarily done enough \u2014 to contest a decision made by an algorithm, an individual or organization would first have to know AI was behind the decision made. But disclosure is lacking.<\/p>\n

In the UK, trade unions are now forming an AI task-force to lobby employers and regulators to increase transparency around where the technology is being deployed, and to offer workers recourse if they believe they are being discriminated against.<\/p>\n

The initiative is being driven by the Trades Union Congress (TUC), an umbrella group for unions representing more than 5.5 million people.<\/p>\n

\u201cWe realized that essentially there\u2019s a revolution taking place in the world of work in terms of the use of AI to manage people,\u201d Mary Towers, an employment rights policy officer at the TUC who\u2019s leading the effort, told Bloomberg<\/em>.<\/p>\n

\u201cWhat we\u2019re really talking about is the use of AI to make decisions that really impact significantly on people\u2019s job opportunities and the nature and fabric of their working lives.\u201d<\/p>\n

Employees speaking to the TUC said they are encountering AI in job applications and interviews, in shift and vacation management decisions, and performance analysis \u2014 areas where AI errors could result in unfair outcomes or discrimination.<\/p>\n

The TUC said that a lack of transparency is compounding the problem. Just 14% of employees thought they would know if AI had made an automated decision about a job application, according to the UK\u2019s Centre for Data Ethics.<\/p>\n

It\u2019s not just employees that are lacking trust in AI. Concerns over the ability to trust AI-enabled decisions could hold back the adoption of the technology more broadly, limiting or delaying its further advance and wider benefits.<\/p>\n

The solution, according to the TUC, starts with unions finding ways to negotiate increasing transparency on where AI is being used, and control over the data that\u2019s collected and used by algorithms.<\/p>\n

AI is a powerful tool, but if we\u2019re not able to show its workings, gaining acceptance will be difficult.<\/p>\n

Last year, Google launched Explainable AI on its cloud platform, a set of tools and frameworks for developing machine learning<\/a> models that allow developers to \u201cvisually investigate model behavior\u201d and \u201cdetect and resolve bias, drift, and other gaps\u201d in machine learning models.<\/p>\n

\u201cMachine learning models can identify intricate correlations between enormous numbers of data points,\u201d said the firm\u2019s Cloud AI Director of Strategy, Tracey Frey in a blog post<\/a>; \u201cWhile this capability allows AI models to reach incredible accuracy, inspecting the structure or weights of a model often tells you little about a model\u2019s behavior.<\/p>\n

\u201cThis means that for some decision-makers, particularly those in industries where confidence is critical, the benefits of AI can be out of reach without interpretability.\u201d<\/p>\n

<\/p>\n

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\"Mark<\/div>\n<\/p><\/div>\n

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1 December 2020<\/p>\n<\/p><\/div>\n

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1 December 2020<\/p>\n<\/p><\/div>\n

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30 November 2020<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n

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Published at Tue, 01 Dec 2020 09:31:05 +0000<\/p>\n","protected":false},"excerpt":{"rendered":"

Why black box AI problem is bad for business Artificial intelligence is a powerful technology,…<\/p>\n","protected":false},"author":3,"featured_media":4014,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_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":[3],"tags":[],"class_list":["post-4016","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/techclot.com\/wp-content\/uploads\/2020\/12\/e0rvQO.png?fit=861%2C484&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p3orZX-12M","jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts\/4016","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/comments?post=4016"}],"version-history":[{"count":0,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts\/4016\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/media\/4014"}],"wp:attachment":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/media?parent=4016"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/categories?post=4016"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/tags?post=4016"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}