Capps warned against high-stakes use of black box AI due to the lack of transparency behind the technology's decision-making process. New developments in artificial intelligence have thrust the technology to the forefront of public discord, but also raised concerns about the opaque decision-making process of some systems – often referred to as "black box AI." The term "black box" came from Great Britain's Royal Air Force during WWII, Dr. Michael Capps told Fox News Digital. But when it relates to AI, the term is used to describe a decision-making process that cannot be explained. "The whole idea of a black box is you're not allowed to look inside and see, and that's what we have with these artificial neural networks, with hundreds of billions of nodes inside of a box, that nobody can look into," Capps said.

There is a key topic that banks spanning the world are asking right now: how are they able to make AI explainable? This was the opinion of Wolfgang Berner, the CTO of RegTech firm Hawk: AI, who recently presented a keynote speech on the above topic. Berner remarked, "In heavily regulated areas such as combating money laundering, considerations as to how transparent and comprehensible the use of artificial intelligence is are entirely appropriate. Classic concerns about such a "black box AI" arise in particular when the decisions of the AI are too disconnected from the original data and when there is no transparency about the way the algorithms work." Hawk AI sees the key to trust and acceptance in the compliance industry in the high level of transparency.

Every time an AI article finds its way to social media there's hundreds of people invoking the terrifying specter of "SKYNET." SKYNET is a fictional artificial general intelligence that's responsible for the creation of the killer robots from the Terminator film franchise. It was a scary vision of AI's future until deep learning came along and big tech decided to take off its metaphorical belt and really give us something to cry about. At least the people fighting the robots in The Terminator film franchises get to face a villain they can see and shoot at. And that makes it difficult to explain why, based on what's happening now, the real future might be even scarier than the one from those killer robot movies.

The rise of AI in business largely goes unquestioned, until a poor decision comes out of a black box that no one can fathom or that causes actual damage. To avoid this, businesses need to adopt AI tools that are provable and customer-friendly, with chatbots paving the way until AI can be truly trusted. In most business cases, artificial intelligence helps companies progress when it comes to their varied use cases. From understanding us humans and our convoluted languages, recovering data from forms, predicting outcomes etc., AI helps spot meaning, intent and value, and provides the power for chatbots, analytic services and other digital business tools. However, as with 5G and 4G before it, as with robots in factories, and those pesky vaccines that keep us alive, there is a narrative in the media that AI is here to destroy us, to wipe out jobs, to weaken employees and other negative outcomes.

The rise of AI in business largely goes unquestioned, until a poor decision comes out of a black box that no one can fathom or that causes actual damage. To avoid this, businesses need to adopt AI tools that are provable and customer-friendly, with chatbots paving the way until AI can be truly trusted. In most business cases, artificial intelligence helps companies progress when it comes to their varied use cases. From understanding us humans and our convoluted languages, recovering data from forms, predicting outcomes etc., AI helps spot meaning, intent and value, and provides the power for chatbots, analytic services and other digital business tools. However, as with 5G and 4G before it, as with robots in factories, and those pesky vaccines that keep us alive, there is a narrative in the media that AI is here to destroy us, to wipe out jobs, to weaken employees and other negative outcomes.

AI has a "black box" problem. We cram data in one side of a machine learning system and we get results out the other, but we're often unsure what happens in the middle. Researchers and developers nearly had the issue licked, with "explainable algorithms" and "transparent AI" trending over the past few years. Black box AI isn't as complex as some experts make it out to be. Imagine you have 1,000,000 different spices and 1,000,000 different herbs and you only have a couple of hours to crack Kentucky Fried Chicken's secret recipe.

Will black box AI fly?Andy Kelly on Unsplash enhanced by CogWorld With current advances in technology and Artificial Intelligence, most major companies are going to great lengths to attract the right talent and showcase their expertise in the field. Today having a futurist among its top rank management is not an eccentric fad, but a competitive necessity. Google is well-known for its collaboration with Ray Kurzweil. While we all know about the AI conquests of IBM, Amazon and Google, less is being revealed about machine learning projects in the "traditional" industry of telecom operators. Coming from a telecommunications background, I thought the balance should be restored.

Intelligence agents and military operatives may come to rely heavily on Machine Learning and Artificial Intelligence (AI) to parse huge quantities of data, and to control a growing arsenal of autonomous systems, but the US Military wants to make sure that this doesn't lead to blindly trusting algorithms, that even though there are a couple of tests to assess how dangerous they are, or could become, are still at their heart mysterious black boxes. As a result the Defense Advanced Research Projects Agency (DARPA), a division of the US Defense Department that explores new technologies, is following the lead shown by Columbia University, MIT, and Nvidia, who have all been trying to develop new systems that read AI's minds and get them to explain their decision making processes, and they've announced they're going to be funding several new projects. The approaches range from adding further machine learning systems geared toward providing an explanation, to the development of new machine learning approaches that incorporate an "elucidation by design." "We now have this real explosion of AI," says David Gunning, the DARPA program manager who is funding an effort to develop AI techniques that include some explanation of their reasoning, "the reason for that is mainly machine learning, and deep learning in particular." Deep learning and other machine learning techniques have taken Silicon Valley by storm, improving voice recognition and image classification significantly, and they are being used in more contexts than ever before, including areas like law enforcement and medicine, where the consequences of a mistake may be serious.

We may already feel cozy about artificial intelligence making ordinary decisions for us in our daily life. From product and movie recommendations on Netflix and Amazon to friend suggestions on Facebook, tailored advertisements on Google search result pages and auto corrections in virtually every app we use, artificial intelligence has already become ubiquitous like electricity or running water. But what about profound and life-changing decisions like in the judiciary system when a person is sentenced based on algorithms he isn't even allowed to see. A few months ago, when Chief Justice John G. Roberts Jr. visited the Rensselaer Polytechnic Institute in upstate New York, Shirley Ann Jackson, president of the college, asked him "when smart machines, driven with artificial intelligences, will assist with courtroom fact-finding or, more controversially even, judicial decision-making?" The chief justice's answer was truly startling.

A winding thread throughout the entire "AI is going to rise up and destroy all humans" narrative is the terrifying concept of deep learning occurring in a black box. How do you provide oversight for a system you can't understand? Just like any other tool, however, it's not whether black box AI represents a danger or not, it's how we choose to use it. The headlines declare this kind of AI untrustworthy, and tell us that experts seek to end its use in government. But what is black box AI?