Both hold great promises, and both have the power to improve efficiency and decision-making processes within local governments. So what exactly are they, and how to they differ ?

Machine vs human?

To put put it bluntly, Artificial intelligence (AI) relies on machines, whereas Collective Intelligence (CI) relies on people. AI stands for the simulation of human intelligence by machines, computers or software systems. In your day to day life, AI is text-prediction in emails, content suggestion in Netflix or traffic prediction in Google Maps. CI, on the contrary is the shared intelligence of multiple people – it is, in a way, the sum of human knowledge (or of a specific community) on a given topic.

Our infographic takes a closer look at the definitions of both artificial and collective intelligence, what their main differences are and how they can reinforce each other .

The difference between AI and CI

Both AI and CI forms transcend the simple individual intelligence, but do so in different ways. AI uses machines and technological development as the way to become smarter as a collective, and CI aims to delve into collective human knowledge to make better decisions. That being said, one does not have to exclude the other. In fact, artificial and collective intelligence can -and should – reinforce each other.

Artificial intelligence can help scale collective intelligence. At CitizenLab, we use AI to help governments harness the power of collective intelligence by giving them tools to manage, analyse and translate thousands of citizen contributions. The algorithms are for instance capable of understanding what comments are about, grouping similar ideas into clusters, placing them on a map and highlighting the main topics discussed on the platform. Without this automated assistance, civil servants would have to manually manage the input – a technical, time-consuming and unreliable task as soon as there is more than a couple dozen contributions.

Collective Intelligence is needed to help keep artificial intelligence ethical and human. Algorithms are as flawed as those who build them, so they regularly need to be checked for bias and prejudice. Moreover, the results found by the algorithms are only ever as good as the initial questions they receive: it is our responsibility to define relevant angles for research and to harness the power of AI for public good.

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More in this series

This article is a part of our “what’s the difference?” series. Browse through the others here: