{"id":13653,"date":"2023-09-13T22:21:58","date_gmt":"2023-09-13T22:21:58","guid":{"rendered":"https:\/\/mysourcefunding.com\/leadership\/pitfalls-in-artificial-intelligence-at-least-for-now\/"},"modified":"2023-09-13T22:21:59","modified_gmt":"2023-09-13T22:21:59","slug":"pitfalls-in-artificial-intelligence-at-least-for-now","status":"publish","type":"post","link":"https:\/\/mysourcefunding.com\/?p=13653","title":{"rendered":"Pitfalls In Artificial Intelligence (At Least, For Now)"},"content":{"rendered":"
\n

Artificial Intelligence, we all know, has tremendous \u2013 incalculable \u2013 potential. but it also poses real threats, mostly due to its newness and rawness, and to our relative unfamiliarity of this new world order. As leaders take their organizations\u2019 first steps into this new wilderness, they would be well advised to temper their eagerness and desire to be early adopters. There are serious pitfalls ahead if not approached with caution. In other words, how fast can you go without the wheels falling off?<\/p>\n

Here’s what to consider.<\/p>\n

Ethical Challenges<\/strong><\/h2>\n

AI can raise complex ethical challenges, such driverless vehicles on the same roads and streets with human drivers), robotic surgery, and urban planning (especially regarding under-represented groups in the population). Deciding how AI systems should behave in morally ambiguous situations is a challenge.<\/p>\n

Regulatory and Legal Challenges<\/strong><\/h2>\n

We always develop new technologies faster than our ability to recognize where regulation is needed or to enact that regulation. This is a challenge for government and the private sector to display common concern and progress.<\/p>\n

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Bias and Fairnessata Bias<\/strong><\/h2>\n

AI systems learn from collecting data, and data is often biased. If an AI system is biased, it most likely lead to biased predictions (which is basically what AI\u2019s output is) and that leads to biased decisions. There is a multiplier effect here, perpetuating and growing inequalities.<\/p>\n

Algorithmic Bias<\/strong><\/h2>\n

Algorithms themselves can introduce bias, and therefore be more hidden than data biases. Nefarious by nature.<\/p>\n

Lack of Transparency<\/strong><\/h2>\n

Producers of AI can be strongly tempted to fight transparency, and that can lead mistrust and difficulties in debugging and maintaining AI systems.<\/p>\n

Privacy Concerns<\/strong><\/h2>\n

We all know that vast amounts of personal data are out there \u2013 data on healthcare, finances, job history, ancestry, insurance, and so on. That, plus algorithmic biases, makes for big trouble.<\/p>\n

Safety and Security<\/strong><\/h2>\n

In critical domains \u2013 especially newly emerging ones like autonomous vehicles or healthcare, where algorithm-based decisions clash with human value-based decision-making \u2013 safety and even deference must rule the day. In many cases, it might not.<\/p>\n

Energy Consumption<\/strong><\/h2>\n

Training large AI models can consume huge amounts of energy; we\u2019ve already seen that in the cryptocurrency universe. When fusion energy takes hold, this will be a non-issue, but we\u2019re 20 years away from that.<\/p>\n

Data Privacy and Ownership<\/strong><\/h2>\n

Who owns and controls data? What liabilities will pop up?<\/p>\n

Job Loss or Displacement<\/strong><\/h2>\n

As AI advances, we are rightfully concerned for our jobs. But as with all major workplace revolutions \u2013 the PC is most relevant \u2013 while some jobs will be lost, far more jobs will be created. They\u2019ll be different, they\u2019ll be in other places, and they\u2019ll merge, but they\u2019ll be there. We just have to get used to looking for them.<\/p>\n

Explainability<\/strong><\/h2>\n

AI makes stuff up. It starts with one word and then predicts \u2013 based on biased data \u2013 what the next word should be ad infinitum<\/em>. That\u2019s what\u2019s called a dumb system. But when you ask AI to explain how it arrived at its answer, it can\u2019t. That requires intelligence, and it exposes AI as an impostor in the field of intelligence.<\/p>\n

Hype and Expectations<\/strong><\/h2>\n

We are overestimating what AI can do now, but underestimating what it will be able to do five years from now. Managing our expectations and using AI appropriately is the challenge.<\/p>\n

Anticipating and addressing these pitfalls requires a common dedication to collaboration among all parties involved \u2013 a difficult assignment under any circumstances It\u2019s the old conundrum of \u201cShould we do what we could do?\u201d<\/p>\n<\/div>\n

Read the full article here<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Artificial Intelligence, we all know, has tremendous \u2013 incalculable \u2013 potential. but it also poses real threats, mostly due to its newness and rawness, and to our relative unfamiliarity of this new world order. As leaders take their organizations\u2019 first steps into this new wilderness, they would be well advised to temper their eagerness and […]<\/p>\n","protected":false},"author":1,"featured_media":13654,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[76],"tags":[],"class_list":{"0":"post-13653","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-leadership"},"yoast_head":"\nPitfalls In Artificial Intelligence (At Least, For Now) | Brandiary<\/title>\n<meta name=\"description\" content=\"Artificial Intelligence, we all know, has tremendous \u2013 incalculable \u2013 potential. but it also poses real threats, mostly due to its newness and rawness,\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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