Helping people address the climate crisis through social wearables and participatory experiences (S1E3)
To think about the climate crisis is to rethink everything built by humans: every intervention, every invention, every time humans ignored nature’s will and played God. This also means reconsidering our relationships with people, things, and the environment. The ways in which we relate, our values, social habits/behaviours and our cultural rituals are all under the microscope.
So, what now? Who is responsible for fixing the damage caused to our planet to ensure we, future generations, other animals/creatures and living entities, have many more years to live on this earth? Is the onus to change our ways of being and doing placed on individuals, communities, policymakers, educators, and governments?
The climate crisis is real and yet so hard to grasp. As designers of digital experiences, products and services, how might we help people address the various dimensions of the climate crisis?
This is what today’s guest will help shed light on through her work that integrates community engagement, collective action, and social wearable technology to attend to the climate emergency.
In this Season 1 Episode 3 of The Art in STEAM podcast, we are joined by Ling Tan — a UK-based Singaporean designer and artist whose works explore citizens’ interaction with the built environment and our collective agency in tackling complex issues using technology.
Listen to the full episode on Spotify, Amazon Music and Apple Podcasts.
[Femmes Designers] You have a few projects that seem to be connected around that: WearON, WearAQ, and the most recent one, Pollution Explorer Collective Action, in that order. How does collective intelligence work with machine learning, and how does it play a role in that project?
[Ling Tan] Publisher Explorer is a project that I've been doing since 2017. So, it started with a series of very low-tech wearables, where we workshop-ed with different groups of citizens in different neighbourhoods using wearable tech to capture their perception of air quality, using very simple gesture-sensing wearables. What happened in that workshop is that people went into the streets, in their neighbourhoods, and we identified different locations in the neighbourhood where we had been collecting data beforehand and used machine learning to help us identify areas where we had missing information on the air quality data itself. So, the way we tried to harness it before the workshop was to get a lot of open-source air quality data and sensor data online using a machine learning algorithm to help us track places where there was missing information. Using that information, we then got people to use their perception to qualify and try to make sense of the missing information. The purpose of it is two levels. The first part was almost like an experiment where we wanted to see how accurate our own sense of the air was, as opposed to a machine’s.
[FD] What do you mean by a subjective sense of the air pollution or the air quality?
[LT] It's about how you feel the air is at that moment. So if you think the air is good, you do a certain gesture; if you think the air is bad, you do a certain gesture and we train, we kind of teach the participant what gesture to use when they think the air is a different thing. So, for example, it's very simple, if the air is good, they raise their hands up, and the wearable will capture the data. If they think that air is bad, they put their hand on their nose, and then the wearable will capture the data as well. Through the workshop, we collect real-time data of what they think the air is at that moment in time at that geolocation. So, we capture those data. While we're doing the workshop, we also use a mobile air quality sensor to capture what the machine says is the air quality at that point.
[FD] To see if it coincides?
[LT] Yes. There were two parts to it. So, there was the front part where, during the workshop, we were doing this, and when we came back from the walk, everyone looked at the data collectively. So, they will look at what most people think in that area. Why is it that people have different understandings of the air quality there? And then we look at the machine data and get them to make a judgement for themselves, whether they think that their sense and their perception is as accurate as the machine. And after the workshop and through running a bunch of these workshops at the same time, around the same period, we then use machine learning to qualify and see how accurate our sensors are, our human sensors, as opposed to a machine. And what was interesting with that experiment is that we found out that, at any instance, a human sensor is 75% accurate. So, what it means is that if you think the ad is good at that point, the chance of it being good is pretty high.
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[Ling Tan] I think, ultimately, we have to look at the way we want to live our everyday lives. And through that, we can then look at how the experience could be changed for the better if we can tap into this aspect for the participant if you see what I mean.
[FD] So, bringing awareness and impacting small changes in people's lives. That's where we can start working on affecting the climate crisis.
[LT] It might sound kind of feasible but not aspirational because when people think about it, they think it's just a small action that you're doing. You're affecting only one person, so why bother to do that? But I think the interesting thing is that when you talk to any active people in climate change, we have to change systemically. We have to affect the government policy. We have to do that to make a big change. But that takes time and years of work to get to that point. Protest movements and social movements can change the needle, but when you do it in a way that alienates the ordinary citizen, you become move everyone along with us.
[FD] Yeah, you can’t guilt-trip people. The world was built like this. It was designed for us to dispose of Tupperware and everything. And now, what do we do? We have to change the way that we do things. But the world has to change around us, too.
[LT] It’s like you said, we can't guilt-trip people because people's actions and everyday lives are affected by the environment they are in. For example, in Tupperware, the use of Tupperware is because some people just don't have access to other things that are more environmentally friendly. So we can't necessarily blame everyone for the things they do with all the conversation about food and how it's impacting climate change because I think it makes up almost 50% of the carbon footprint, in terms of the consumption of food (especially with meat, which everyone knows by now that red meat is the one that has incurred the most amount of carbon footprint). But if we look at the working class where to have a meal on the table, they have to go to McDonald's to buy food that is high in processing and high in carbon footprint, but because it's cheap, how do we make sure that people, everyone can afford to go through that route of being more environmentally friendly? Because you have people who are vegan, who are vegetarian, if you look in London, people who took on that movement were probably the middle class.
