To err is algorithm: Algorithm fallibility and economic organisation

Dig below the surface of some of today's biggest tech controversies and you are likely to find an algorithm misfiring:[1] These errors are not primarily caused by problems in the data that can make algorithms discriminatory, or their inability to improvise creatively. No, they stem from something more fundamental: the fact that algorithms, even when they are generating routine predictions based on non-biased data, will make errors. We should not stop using algorithms simply because they make errors.[2] Without them, many popular and useful services would be unviable.[3] However, we need to recognise that algorithms are fallible, and that their failures have costs. Economics is the science of trade-offs, so why not think about this topic like economists? This is what I have done ahead of this blog, creating three simple economics vignettes that look at key aspects of algorithmic decision-making.[4] The two sections that follow give the gist of the analysis and its implications.