But beyond these tenets is another crucial element about the potential of tech for helping find new approaches to persistent social challenges.
How do we re-view the sorts of challenges we’re looking to address through a digital lens?
My favourite way of describing of the difference between digital and analogue is the difference between a trombone and a trumpet.
Trombonists find a note by moving to their arm (and the size of the trombone tubes) to a certain size. The better they get, the more specific they can be, which takes practice, a good ear. But it’s an approximation, a sliding scale between other notes to find the one you’re after. A trumpet acts differently. The valve is either pressed or it isn’t, which means the note is either played or it isn’t. Rather than an approximation, it’s a binary choice. Sure, there are a number of different binary choices that come together and that means you can play a whole range of notes, but at the heart of it is a binary choice of a valve pressed or not.
Digital, like the trumpet, is made up of a lot of binary choices. Together, multiplied by billions, those binary choices create the complexity of websites and apps, of bots and AI. So, as we take a digital-look at social challenges, incredibly complex as they are, we try to describe them as lots of more simple, interacting choices. This isn’t to dilute the challenge, but to find addressable elements.
Why is this important? Because the sorts of tools and technologies available to us influence not only how we can address problems, but how we can conceptualise them.
Within the tech for good community we see design processes that address social challenges through computational approaches – looking to reduce complex social challenges to the smallest solvable part. That’s because to create a digital response, in some way, we need to able to explain the smallest component parts of a solution and how they build together. This can provide clarity when the challenges we’re trying to address are amorphous and complex – ‘poverty’, ‘social justice’ or ‘care’ – and fundamentally it provides a new way to conceptualise and then address complex social challenges. To use Marc Andreesson’s phrase: “It’s a bottom-up reinvention of the fundamental assumptions of how these things work.”
Shift’s BFB Labs is a great example of this. Their starting point is to support better mental health amongst young people. This broad and lofty goal is reduced through a really strong theory of change that moves from supporting young people to have more positive mental health experiences to being able to regulate their emotional state via their breathing. The resulting game takes the micro-mechanism of practising diaphragmatic breathing as the yes/no to playing the game, and the yes/no for a more positive mental health experience.
Reducing the big challenge of enabling more positive mental health experience down to breathing exercises whilst playing a computer game is a great example of reducing the size of the social challenge to something specific and actionable. Of course, it doesn’t ‘cure’ poor mental health (though they’ve good signs that these techniques are usable outside of the game play) but it addresses a significant part of the challenge (and allows others to focus on alternative parts of the bigger challenge).
Huge, vague challenges, reduced to the smallest addressable feature. As close as we can come to finding the yes/no binary. There might be some push-back to how such complexity as social care or mental health can be reduced so much, but conversely artificial intelligence, complicated websites and databases – even computers that beat the world champions at chess or the even more complex Go – are made up of billions of yes/no binaries. Whether it’s the solution, or part of a solution – we have a new approach to considering social challenges through the lense of digital.
I’d argue that this approach provides a new way to conceptualise and then address social challenges. If on reading this you think such a digital approach is too reductive, then perhaps you could spend some time listening to what happens when lots of binary values are pressed, and not pressed, by experts.