Artificial intelligence (AI) is cropping up everywhere in business, as organizations deploy the technology to gain insights into customers, markets and competitors, and to automate processes in almost every facet of operations. But AI presents a wide range of hidden dangers for companies, especially in areas such as regulatory compliance, law, privacy and ethics. There is little visibility into how AI and machine learning technologies come to their conclusions in solving problems or addressing a need, leaving practitioners in a variety of industries flying blind into significant business risks. The concerns are especially relevant for companies in industries such as healthcare and financial services, which have to comply with a number of government and industry regulations. "Context, ethics, and data quality are issues that affect the value and reliability of AI, particularly in highly regulated industries," says Dan Farris, co-chairman of the technology practice at law firm Fox Rothschild, and a former software engineer who focuses his legal practice on technology, privacy, data security, and infrastructure matters.

Unequivocally, the prominences of robotics, AI and a digitized banking landscape have many people worried about the influences these trends make on banking.

Late last year, a St. Louis tech executive named Emre Şarbak noticed something strange about Google Translate. He was translating phrases from Turkish -- a language that uses a single gender-neutral pronoun "o" instead of "he" or "she." But when he asked Google's tool to turn the sentences into English, they seemed to read like a children's book out of the 1950's. The ungendered Turkish sentence "o is a nurse" would become "she is a nurse," while "o is a doctor" would become "he is a doctor." The website Quartz went on to compose a sort-of poem highlighting some of these phrases; Google's translation program decided that soldiers, doctors and entrepreneurs were men, while teachers and nurses were women.

Intraspexion uses deep learning and predictive analytics to predict and prevent potential litigation. Intraspexion is as an early warning system that operates through analysis of a company's emails to identify those that contain risk factors. Legal lawsuits cost companies in the USA $150 billion each year. Deep learning is used to identify specific, potential risks to an enterprise (of which litigation is the prime example) while such risks are still internal electronic communications. The system involves mining and using existing classifications of data (e.g., from a litigation database) to train one or more deep learning algorithms, and then examining the internal electronic communications with the trained algorithm, to generate a scored output that will enable enterprise personnel to be alerted to risks and take action in time to prevent the risks from resulting in harm to the enterprise or others.

The era of big data led to companies all over the world embracing data as a key competitor driver. The more they knew about their operations, customers and products, the more successful they would be.

Artificial intelligence (AI) is cropping up everywhere in business, as organizations deploy the technology to gain insights into customers, markets and competitors, and to automate processes in almost every facet of operations.

Just as the Industrial Revolution transformed the world during the 18th and 19th centuries, we are facing the dawn of an equally far-reaching artificial intelligence or AI revolution that will be measured in years rather than decades. AI has reached the point where it is capable of surpassing the decision-making of humans in many situations; consistently, accurately, 24/7 and based on more facts. But why now and how do businesses harness this capability? AI has been around since the 1960s; only now is the confluence of three key factors coming together. Firstly, AI has languished as a "disembodied brain in a jar", isolated from the real world.

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A number of this week's milestones in the history of technology demonstrate society's reactions to new technologies over the years: A discussion of AI replacing and augmenting human intelligence, a warning about the abundance of misinformation on the internet, and government regulation of a mass communication platform, suppressing free speech in the name of the public interest. On February 20, 1947, Alan Turing gave a talk at the London Mathematical Society in which he declared that "what we want is a machine that can learn from experience." Anticipating today's enthusiasm about machine learning and deep learning, Turing declared that "It would be like a pupil who had learnt much from his master, but had added much more by his own work. When this happens, I feel that one is obliged to regard the machine as showing intelligence." Turing also anticipated the debate over the impact of artificial intelligence on jobs: Does it destroy jobs (automation) or does it help humans do their jobs better and do more interesting things (augmentation)? Turing speculated that digital computers will replace some of the calculation work done at the time by human computers.

Imagine your Aunt Ida is in an autonomous vehicle (AV) -- a self-driving car -- on a city street closed to human-driven vehicles. Imagine a swarm of puppies drops from an overpass, a sinkhole opens up beneath a bus full of mathematical geniuses, or Beethoven (or Tupac) jumps into the street from the left as Mozart (or Biggie) jumps in from the right. Whatever the dilemma, imagine that the least worst option for the network of AVs (Ed: Autonomous Vehicles) is to drive the car containing your Aunt Ida into a concrete abutment. Even if the system made the right choice -- all other options would have resulted in more deaths -- you'd probably want an explanation. Or consider the cases where machine-learning-based AI has gone wrong. It was bad when Google Photos identified black men as gorillas. It can be devastating when AI recommends that black men be kept in jail longer than white men for no reason other than their race. Not to mention autonomous military weapon systems that could deliver racism in airborne explosives.