47 FUTURE SHOCK Artificial Intelligence
ince Babbage’s Analytical Engine in 1837, the purpose of computers has been to do the donkey work for human beings. Machine
“And yes, they can do that. But they have to be really careful.” WHAT’S INSIDE THE BLACK BOX? One concern about machine learning is that while it can draw powerful conclusions and make decisions, it offers no hints as to how it has come to those conclusions. Machine learning is a ‘black box’ process, where humans can monitor the inputs and outputs, but precisely how those numbers are crunched is obscure. As with a human being, the machine makes choices based on the sum of its experiences – or the sum of the data supplied to it as training examples. But delving into the resulting configuration of the machine reveals something that looks, to the outsider, like noise and might be so complex as to completely defy any attempt at analysis. And unlike a human being, the machine cannot explain its decisions. The impenetrability of AI is a subject well known to Adriane Chapman, associate professor of Computer Science at the University of Southampton. Chapman describes her area of interest as “how we process our data to make it useful”. “Machine learning,” says Chapman, “is a hugely powerful tool, but within machine learning there are many different techniques. In general these have, until now, been treated very much as a black box. Introspection is hard - we’ve built this tool, it does this thing, but in terms of neural networks, one answer pops out of the end and it’s not easy to determine why. Not impossible, but not easy.” This might seem like a lot of concern over a comparatively minor issue – after all, who cares why an AI reaches a particular decision, so long as that decision is right? But no machine learning conclusion will ever be completely error- free. In some circumstances a decision might be challenged and, in order to defend that decision, it will be necessary to have some understanding of how it was reached. As a result, AI might be employed for all sorts of tasks – simply based on expediency – for which it may not be suitable at all.
learning is already making it possible for computers to do vastly more of that work: for the media industry alone, the ability to catalogue thousands of hours of footage without requiring thousands of hours of human time is a revelation. But artificial intelligence is only as intelligent as the people training it, and it has occasionally shown itself to be supremely stupid, with one of its most notorious public errors being Google technology tagging images of black people as ‘gorillas’ in 2015. Google’s solution to the problem was ultimately simply to remove the terms ‘gorilla’, ‘chimp’, ‘chimpanzee’ and ‘monkey’ from its image categories. Stuart Coleman is founder of infoNation, a consultancy focusing on data and technology, and a former director of the Open Data Institute. He describes himself as “a technologist” who has spent a lot of time around early internet technology. In recent years, he has focused on implementing data science tools and techniques. “There’s a lot of people in the last few years talking about AI. It’s a bit like ‘big data’ three years ago,” says Coleman. Despite the publicity around Google’s gorilla gaffe, Coleman observes that AI problems are generally kept behind closed doors. “The power of machine learning and data science tools and technologies is held by a small selection of companies. Though the tools are being adopted in the mainstream, there hasn’t been enough diversity of activity to see what goes wrong – and the big companies then, demands just the sort of care that companies might be tempted to skip in the face of huge potential savings – the very savings machine learning promises to deliver. “I just came from a meeting with a major company who want to wipe out 30% of their workforce,” continues Coleman. aren’t sharing what goes wrong.” Getting machine learning right,
FAIRNESS IS A SOCIAL AND ETHICAL CONCEPT. IN MACHINE LEARNING WE OFTEN TREAT IT AS A STATISTICAL CONCEPT
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