If you\u2019ve never heard the term \u201calgorithmic bias\u201d before, you\u2019re not alone. Despite many years in pursuit of the powers of machine learning and AI capability, the idea that human bias influence the algorithms we create is one we\u2019ve only recently acknowledged. <\/p>\n
So, what is it really?<\/p>\n
Algorithmic bias refers to the biases an algorithm picks up from its human creators, similar to how a child does from its parents. As artificial intelligence and machine learning have jumped out of science fiction into the real world, the potential negative impacts of biases in our systems have grown larger and more imposing.<\/p>\n
But before we even begin discussing such a topic, we have to first break through a key myth most people have about computers and their programs: that they are objective. <\/strong>In reality, algorithms are as susceptible to influence as humans are because we\u2019re the ones building them from the ground up in the first place. They don\u2019t exist as entities in their own right, but as extensions of the human mind\u2014just with more processing power.<\/p>\n That all sounds like a no-brainer when broken down that way, but there\u2019s more. Here, we\u2019re going to look at how machine bias may come about and the very real effects it has on the lives of people living today.<\/p>\n Cognitive bias<\/em><\/p>\n Definition: (noun) systematic patterns of deviation from norm and\/or rationality in judgement.<\/em><\/p>\n Before we get into machine bias, let\u2019s look at bias from a perspective a little closer to home\u2014the ones that exist in our own minds. Cognitive biases are a recognised and highly studied field of modern psychology, touching on everything from the understanding of patterns to the influence of racism.<\/p>\n Basically, the human mind evolved to recognise patterns. It\u2019s just what we do. The problem is that we are taught to irrationally see certain patterns over and above others. Eventually, we tend to ignore contradictory evidence, passing over blind spots in favour of sitting in our comfortable biases. After all, it\u2019s human nature to enjoy a cosy comfort zone.<\/p>\n Some examples of cognitive biases include:<\/p>\n Keep in mind that most cognitive biases are almost entirely unconscious, like many of our psychological processes. They fly under the radar, which is why it\u2019s so hard to detect them when it comes to building algorithms that eventually include those same biases.<\/p>\n <\/p>\nA quick look at cognitive bias<\/h2>\n
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How we raise an algorithm, and what we feed it<\/h2>\n