AI is extremely powerful. Its algorithms and detailed processes allow for unprecedented accuracy when it comes to results; results more accurate than even the human brain can produce. Take Tinder as an example; the app that revolutionised the world of dating by simply removing the middleman: the “introducer”. In the same breath, Uber has replaced hailing black cabs, mobile banking threatens cashiers and apps like Deliveroo and Just Eat have changed the way we order food. Taking the luck out of gambling With AI, it has become possible to remove a significant chunk of the “luck” element of gambling. For example, imagine if statistical processes and algorithms were used to predict the outcome of sporting events, such as football and horse racing. Bettors would be able to make decisions by factoring in various statistics. This is a unique approach, and one where man and machine are working together to predict the outcome of a sporting event. As gambling is an industry that is centred around probability and statistics, it makes perfect sense to integrate AI into predicting whether there will be over or under 3.5 goals in a football match. Thanks to these applications, we have become a generation that simply run our lives through a device; in fact, we have never been more independent. The immediacy and accessibility promised by technology means that the middleman has been stripped back time and time again. This is where Big Data comes in Big Data has generated new tools and ideas on an unprecedented scale, with applications spreading from marketing to human resources, to university applications, insurance and much more. At the same time, Big Data has opened opportunities for a whole new class of professional manipulators, who take advantage of people using the power of statistics. Algorithms run everything from taxis, to advertising, to who we end up going on a date with. They’re used to sift through CVs, check our credit and decide whether we’ll get insurance. In a nutshell, Big Data has turned information into power. This data helps build tailor-made profiles that can be used for or against someone in a given situation. Insurance companies, which historically sold car insurance based on driving records, have more recently started using such data-driven profiling methods. In fact, it’s been suggested that some insurance companies are charging people with low credit scores and good driving records more than people with high credit scores and a drunk driving conviction. It’s become standard practice for insurance companies to charge people not what they represent as a risk, but what they can get away with. The victims, of course, are those least likely to be able to afford the extra cost, but who need a car to get to work. But algorithms aren’t inherently evil: they’re tools used to simplify decisions, increase efficiency and offer convenience. But when locked away we can’t understand how they work or even if they work at all. Consider online recruitment; this is saving companies tons of money in human resources hires but they are also almost entirely opaque. In other words, the process is treated as a money-saving black box, but it’s not clear what that black box is actually doing. However, more employers are turning to these modelled ways of sifting through job applications. Even when wrong, their verdicts seem beyond dispute. Read more about algorithms in the workplace…
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