Civilisations evolve through strategic forgetting of what were once considered vital life skills. After the agrarian revolution of the Neolithic era, a farm worker could afford to let go of much woodland lore, skills for animal tracking, and other knowledge vital for hunting and gathering. In subsequent millennia, when societies industrialised, reading and writing became vital, while the knowledge of ploughing and harvesting could fall by the wayside.
What’s new, however, is that many of our memory partners are now smart machines. But an AI – such as Google search – is a memory partner like no other. It’s more like a memory ‘super-partner’, immediately responsive, always available. And it gives us access to a large fraction of the entire store of human knowledge. Researchers have identified several pitfalls in the current situation. For one, our ancestors evolved within groups of other humans, a kind of peer-to-peer memory network. Yet information from other people is invariably coloured by various forms of bias and motivated reasoning. They dissemble and rationalise. They can be mistaken. We have learned to be alive to these flaws in others, and in ourselves. But the presentation of AI algorithms inclines many people to believe that these algorithms are necessarily correct and ‘objective’. Put simply, this is magical thinking. The most advanced smart technologies today are trained through a repeated testing and scoring process, where human beings still ultimately sense-check and decide on the correct answers. Because machines must be trained on finite data-sets, with humans refereeing from the sidelines, algorithms have a tendency to amplify our pre-existing biases – about race, gender and more. An internal recruitment tool used by Amazon until 2017 presents a classic case: trained on the decisions of its internal HR department, the company found that the algorithm was systematically sidelining female candidates. If we’re not vigilant, our AI super-partners can become super-bigots.