Algorithms Are Moulding and Shaping Our Politics. Here's How to Avoid Being Gamed TC_Africa: SouthAfrica
Before Bayes, computational processes relied on frequentist statistics. Most people have encountered this method in one way or another, as in the case of how probable it is that a coin will land heads-up and tails-down. This approach starts from the assumption that the coin is fair and hasn't been tampered with. This is called a null hypothesis.
Bayesian statistics does not require a null hypothesis; it changes the kinds of questions asked about probability entirely. Instead of assuming a coin is fair and measuring the probability of heads or tails, it asks us instead to consider whether the system for measuring probability is fair.
That's because Bayesian approaches use prior distributions, knowledge or beliefs as a starting point of probability. Unless you change your prior distributions, the algorithm will continue providing evidence to confirm your initial measure of subjective belief. But how can you know to change your priors if your priors are being confirmed by your search results all the time? This is the dilemma of algopopulism: Bayesian probability allows algorithms to create sophisticated filter bubbles that are difficult to discount because all your search results are based on your previous searches.Get the latest in African news delivered straight to your inbox
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