This "smartification" of everyday life follows a familiar pattern: there's primary data – a list of what's in your smart fridge and your bin – and metadata – a log of how often you open either of these things or when they communicate with one another. Both produce interesting insights: cue smart mattresses – one recent model promises to track respiration and heart rates and how much you move during the night – and smart utensils that provide nutritional advice. In addition to making our lives more efficient, this smart world also presents us with an exciting political choice. If so much of our everyday behaviour is already captured, analysed and nudged, why stick with unempirical approaches to regulation? Why rely on laws when one has sensors and feedback mechanisms? If policy interventions are to be – to use the buzzwords of the day – "evidence-based" and "results-oriented," technology is here to help.
Regulations, which specify how to execute those laws in much more detail, should be regarded in much the same way that programmers regard their code and algorithms, that is, as a constantly updated toolset to achieve the outcomes specified in the laws.A brief exchange with Tim O’Reilly about “algorithmic regulation”
Increasingly, in today’s world, this kind of algorithmic regulation is more than a metaphor. Consider financial markets. New financial instruments are invented every day and implemented by algorithms that trade at electronic speed. How can these instruments be regulated except by programs and algorithms that track and manage them in their native element in much the same way that Google’s search quality algorithms, Google’s “regulations”, manage the constant attempts of spammers and black hat SEO experts to game the system?
Revelation after revelation of bad behavior by big banks demonstrates that periodic bouts of enforcement aren’t sufficient. Systemic malfeasance needs systemic regulation. It’s time for government to enter the age of big data. Algorithmic regulation is an idea whose time has come.
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