Designing A Future Where Everyone Counts

 

 

 

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About This Episode

Jutta Treviranus pushes back against cultures, policies, and technologies designed only for those deemed “normal.” As founder and director of the Inclusive Design Research Centre at Ontario College of Art and Design, Treviranus works to challenge and improve the way we build software, make large scale decisions, and govern. If only the majority matters, she says, innovation is stifled, and there will always be people left marginalized. Designing a future where everyone counts requires a different calculus.

 

 


 

 

“It’s not the majority that tells us what are the disruptive moments. You can see it in what are the innovations—all of the innovations came by looking at the edge and by trying to address the needs of the edge.”

 

 


 

 

In This Episode

 

Jutta Treviranus, Founder and director of the Inclusive Design Research Centre at Ontario College of Art and Design

 

 

 

 

Laura Flanders:

 

Average, normal, the mainstream most often occurring, personal condition or taste. Over her long career, Jutta Treviranus has been pushing back against culture’s policies and technologies that’s lead with the norm. “Any one of us,” she says, “due to birth situation or accident could find almost critical needs falling outside of what is deemed average and going unrecognized, unserved, unseen.” As founder and director of the Inclusive Design Research Center at the Ontario College of Art and Design University, Jutta’s work challenges the way we build software, make large scale decisions, and even govern. “If the majority rules,” she says, “then there will always be someone left marginalized.” It’s all coming up on the Laura Flanders Show, the place where the people who say it can’t be done take a back seat to the people who are doing it. Welcome.

Laura Flanders:

 

Jutta, I’m very glad to have you in the studio. We first met many years ago and it triggered something in me that has been a really interesting, led me on a very interesting journey. Where did thinking about these questions start for you?

Jutta Treviranus:

Ah, well we’ve been looking at the tension between universal design within architecture and within industrial design where what we have to do in order to stretch, to encompass a whole range of needs, we have to create one size fits all systems, which of course is a very difficult process because we’re so diverse. We have so many different needs. And it started when, actually I had this fairly optimistic period in 79.

Laura Flanders:

 

That’s going back.

Jutta Treviranus:

That’s going back quite a ways. The first thing that came out in 79 were personalized computers and at the time I was working with 12 people that wanted to attend university but had a variety of disabilities and therefore couldn’t attend and I thought, oh wow, here are translation devices. They can translate what I’m able to produce as speech into something that’s recognizable. They can translate what I can’t see into something that I can sense. Then the next wave of innovations came out, the web and here there was the opportunity to collectively band together and create resources.

Jutta Treviranus:

But since then, I’ve been seeing how these have been co-opted, how all of the values that we originally thought of are actually going in the opposite direction. It’s not greater inclusion. It’s not benefiting people who are diverse. We’ve sort of transitioned into popularity data and we’re going back to, or we’re going against all of the things that we were hoping these technologies could do.

Laura Flanders:

 

When you’re talking about universal design or designing for majorities or for the most often occurring data point, a lot of people would say, “Well, that’s normal. That’s how you make sure your service fits most people.”

Jutta Treviranus:

Well those ideas actually came, we think that technology’s so disruptive. That we’re doing something so new, and yet those ideas came from the early 1800s when we started the first wave of big data and demographers started to measure people. There wasn’t this notion of average until Quetelet invented what he called the average man. And he was a fairly racist, bigoted person.


Laura Flanders:

 

Selfish, competitive.

Jutta Treviranus:

Who said that anything that was not actually average, he said was a monstrosity. It was a deviation. The perfect person was the average.

Laura Flanders:

 

What year are we talking about?

Jutta Treviranus:

We’re talking early 1800s and he was a compatriot of Dewey and also Darwin, but of course, but I think Darwin was somewhat misinterpreted. It’s actually Darwinism that we took from that. But what it resulted in is a whole set of ideas that have become unconscious and we’re not recognizing when we use it, it’s so entrenched in our ethos. About the normal, the average, I would challenge anyone to listen to themselves and monitor to themselves how often we use it. And it’s had massive implications, not just for people that are not average or not typical, but also for us as a society.

Laura Flanders:

 

Describe some. How so?

Jutta Treviranus:

Because of the way that we’ve been looking at how we make decisions, how we govern, how we determine what is truthful and what is evidence, we are compelled to think about only the largest customer base or the statistical average.

Laura Flanders:

 

If you have a lot of likes it must be true.

Jutta Treviranus:

Yeah, popularity.

Laura Flanders:

 

Or a lot of people say the thing, it gets greater credibility. If everyone’s buying, it must be good.

Jutta Treviranus:

Yeah, exactly. And so what’s happening is we are creating disparity because the people that do not fall into those categories that are not popular, that are not part of the majority, that are not a part of the metrics because what you need or that are diverse. The way that I like to talk about it is we collected this data set of needs and requirements because we were trying to create a platform that would serve unmet needs at the margins with the skills and opportunities of people that couldn’t find their way into the labor force.

Jutta Treviranus:

And so we had all these data points of what is it that you need? What’s critical and essential to you? And so if we took that data set and we’ve plotted it on a multivariate scatterplot, it looks like a human starburst where you have 80% of the needs clustering in the middle, taking up 20% of the space and then the remaining 20% are scattered across the rest of the starburst taking up 80% of the unexplored terrain. And so what’s been happening with our manufacturing systems, with our knowledge, with everything, work, education, democracy is that whatever we design works for that middle 20% of the terrain or if your needs are within that middle 20% and that’s 80% generally of the individuals. And as you move away, it starts to become difficult and if you’re at the edge, if your needs are at the edge, then it doesn’t work. Out of that have come the waste that we have because we’re all very different.

Jutta Treviranus:

What it compels us to do is to create a one size, a single sort of structure for the middle. It doesn’t cause us to stretch or to innovate and it doesn’t allow us to see the weak signals, especially in our knowledge systems. And so we are reducing the innovation. One of the things that is frequently talked about when we’re talking about the push for progress and the entrepreneurial sort of spirit is that Friedman curve where we say that technology is adapting exponentially, people are adapting linearly and that’s supposed to encourage us to become more digitally literate, for people to adapt much more quickly to the technology. But I think it’s actually a worse situation because I think the way that we’ve been designing our technologies is causing people to adapt less because we’re exposed to, we’re not exposed to dissonance, we’re not exposed to diversity.

Jutta Treviranus:

Recommender sites, the popularity push that we’re talking about means that we are making ourselves more and more comfortable. We’re not adapting to the diversity that is around us. And I would say that there is another curve that’s very similar and that technology is connecting exponentially. We’re creating this mesh of connected technology and yet our technical designs and our technical systems are actually causing us to connect less as humans, whether it’s device mediated systems or whether it’s the disparity, the polarization that’s happening.

Laura Flanders:

 

There’s a huge amount in what you just said and one of the things I think about is when you are trying to provide or produce for a mass, a mass system and your country is as big as the United States, it sounds like what you’re talking about is not unrelated to the growth of these enormous monopolies and the disparity between rich and poor. Because of just by virtue of the matter of scale. The other question that I have though for you or the other thing that makes me think about is we’re talking about popularity and inaccuracies but your research has actually been about human life and driverless cars, things like that. This isn’t just conceptual failure.

Jutta Treviranus:

No, no. Yeah, I had one really pivotal moment when it just hit me, whoa, where are we going? What are we doing here? Especially as a researcher that’s looking at data. I was able to test six driverless car learning models. These are the learning models that are going to tell cars, stop, change direction or continue through an intersection. And I thought, okay, I’m going to test them with something unexpected, something unusual. I had a capture of a friend of mine who pushes her wheelchair backwards through an intersection. She’s really fast, she’s very efficient. But most people think, oh my gosh, she’s lost control. And they’ll grab her and try to push her back to where she came from. All six of them decided to run her over. They all said, “Wait, wait. It’s okay. These are not smart enough. We haven’t exposed them to enough data.”

Laura Flanders:

 

The companies all said we just didn’t have it quite right. Come back.

Jutta Treviranus:

Right, exactly. Come back.

Laura Flanders:

 

We’ll get better.

Jutta Treviranus:

We’ll get better. We’ll make them smarter. We’ll teach them more about people in wheelchairs in intersections. And so I came back after they had exposed them to a lot of data on people in wheelchairs in intersections. They thought, they reported to me that they were much smarter. What happened is they ran her over with greater confidence because all the data told them to be confident that people in wheelchairs go forward when they go through intersections. The smarter systems are actually driving the smarts to not be cognizant of the margins, not to be cognizant of those weak signals and those unexpected things.

Laura Flanders:

 

Is that as simple as algorithmic bias, which you’ve talked about in this program before? That your outcome is only as good as the data that you put in?

Jutta Treviranus:

Well, so this is what worries me because there’s lot of attention at the moment to AI ethics and the two main things we’re concerned about with AI ethics is let’s remove human bias because we are biased. We are, if we take data about anything that we’ve done in the past, there is racism, there’s sexism, there’s all of those things and let’s remove that. Even if ideally we could remove all that and the other area that they tried to cover is the data gaps. It’s highly unlikely that if you have a disability, if you’re old, if you live in a rural remote community, if you’re not literate, that you’re going to be part of a data set because you may not have the devices and researchers are less likely to collect the data on you. Researchers usually exclude you if there’s anything that might be unusual. We can address those data gaps and, but if ideally we could have proportional representation, we could remove all bias, we’re still in trouble.

Laura Flanders:

 

Why?

Jutta Treviranus:

Because the data that, how we analyze the data and how we make decisions based upon that data is by majority rules and by statistical significance. Which means that if you are not like the average, then probability is wrong. It’s just wrong and the decisions will be against you. And it doesn’t serve us as a society or as humanity at all because we are therefore actually contracting, we’re taking our focus in. We used to be pioneers. And when we think about what does it take to survive as a society? It takes pioneering, it takes new innovative thinking, it takes evolving. But we’re devolving to some extent because we are pulling all of our insights, all of our focus into the average or the typical. And so we miss the weak signals.

Laura Flanders:

 

In the media world in which I operate, the way that that plays out is that we are told and when Facebook was putting in its new ways to combat fake news, they said, “Well we’ll make sure that we use majorities, mainstream sources.” The whole idea of mainstream and they’ll be the ones that are prioritized. And I thought to myself, well if somebody like Ida B. Wells was around today, she would never make it into Facebook’s algorithms because she was saying the exact opposite of what everybody had been saying about lynching. Her analysis of what was happening, her reporting of what was happening was that weak spot or strong spot, depending on how you look at it. She was sounding an alarm which by nature means a new sound that you haven’t heard before or not enough people have heard.

Jutta Treviranus:

Right, exactly.

Laura Flanders:

 

How do you do this differently? And what are the implications for democracy? You’re kind of dodging around it, but I’m kind of hearing democracy isn’t a good principal.

Jutta Treviranus:

Well it depends how we interpret democracy.

Laura Flanders:

 

Okay, go for it.

Jutta Treviranus:

Democracy needs to be more than one person, one vote. Because the issue is that the only thing that we’re doing is we’re counting the number of people as opposed to giving weight to the value or the criticalness of what it is they require. If I have a critical need and I’m trying to find a representative to address my critical need, and my neighbor just wants the pothole in front of her driveway to be fixed because it’s really bothersome. The better off our society society becomes, the more likely through our democratic system at the moment, we are going to address the needs of the neighbor with the pothole, not the needs of the person who has a critical need that is going to save their life because there will be less people aware of that and there will be less people that will be voting for that.

Jutta Treviranus:

We need to think about democracy in a much more nuanced way. And I know it’s a really, really dangerous time to be criticizing democracy. But I think the problem is that when things get polarized and when we’re under attack, we retrench and we lose self critique. And I think the main thing we need to do as a progressive society, as individuals that are concerned about democracy, is to think about how we can evolve democracy and address the issues that we have.

Laura Flanders:

 

Are there models coming from the world of criminal justice? I had Dean Spade on the show at one point who talks about trickle up justice and Kimberly Crenshaw talks about it too. But these are two progressive legal thinkers who were saying, “We don’t want rights that we make for the vast majority and then add other people in. We need to secure the rights of the most vulnerable and the outliers. And in doing so, ipso facto, we’re creating a better safety net for everybody.” But is that directly applicable? Can governments actually afford to do outlier driven planning?

Jutta Treviranus:

Well, I think it’s the only way that we’re going to be able to predict what is coming. And it’s not the majority that tells us what are the disruptive moments. You can see it in what are the innovations, all of the innovations came by looking at the edge and by trying to address the needs of the edge, whether it’s email or even the web or the internet and things of that nature. All of the things that have in essence caused us to evolve. And there are scientists that are looking at things like Darwin and how did humans evolve? Well, humans evolved not during periods of natural selection when there was a huge amount of competition and we got rid of the weak, which is usually what we think of as Darwinism. It was actually when we had relaxed natural selection and we had a greater variety of choice to choose from. The objection that people frequently make to that outlier viewpoint is that, well, are we giving into extremism? Are we letting…

Laura Flanders:

 

The guy at the back of the room screaming.

Jutta Treviranus:

Right. Yeah. But that’s not in fact the case at all. Because what happens, we prevent polarization and we prevent extremism by being exposed to a variety of views. Polarization happens when we cushion ourselves from diverse perspectives. If we have diverse perspectives and if we make room for diverse perspectives, we allow the greatest diversity and support the greatest diversity, then we’re going to reduce extremism and polarization.

Laura Flanders:

 

What the white anti racist upsurge would call calling in as opposed to calling out. How do we do it? You have any examples?

Jutta Treviranus:

Yeah. Well I think, so we have lots of examples. They’re all experiments of course is this is new territory.

Laura Flanders:

 

It’s the age we’re in.

Jutta Treviranus:

We’re pioneering.

Laura Flanders:

 

In a non-colonial kind of a way, non seizing land. Yes, got it.

Jutta Treviranus:

Yes, yes, yes, yes, yes. Exactly. Exactly. Yes. Yeah. Yeah. Thank you. Of course the other critique we frequently get is how do we measure? But the way that we’re trying to do this is through bottom up co-design. We have the three dimensions of inclusive design. First of all, we have to start out by recognizing that we’re all very, very different. Even those group of people that are in that middle, that are average, that are supposedly typical, there isn’t any average us. There isn’t even an average you. Don’t believe that Myers Briggs or whatever, what color is my whatever.

Laura Flanders:

 

My color scheme.

Jutta Treviranus:

Those personality typing things because we change depending upon our goal, the social context.

Laura Flanders:

 

Part of day in my case.

Jutta Treviranus:

Yeah, exactly. How old we are, what our experience was. We need to recognize that we’re all very diverse and we need to recognize the diversity in an integrated way. We can’t say, “I’m going to deal with those that have economies of scale and then the rest afterwards if I get to them.” It has to be integrated. We also need to make sure that the knowledge about how we’re different is imparted to the individual and that because we need a self awareness of our diversity. The second dimension that we have is we have to create inclusive processes for our decision making, for our designing so that everybody can come to the table. What does the table look like? How do we design the table?

Laura Flanders:

 

More like public assemblies, perhaps.

Jutta Treviranus:

Public assemblies, but a whole diversity of ways of contributing your views. And we can’t, it’s not just about inviting people to a place that they didn’t help to design. I think that’s one of the unfortunate ways in which equity and social inclusion have been addressed lately. Where we decide we’re going to hire a whole bunch of people that we haven’t hired before, but they come into a culture and they come into a set of systems that were not made with or for them. And so there are friction points and it’s a way of saying, “I told you so, it doesn’t work.”

Laura Flanders:

 

Women just don’t last in the workplace. We don’t know why. They leave mid career. Why could that be?

Jutta Treviranus:

Exactly. We need to invite people to help us design those tables. And that means a very, very democratized set of tools, whether it’s coding, whether it’s how to contribute within education, how to do research in such a way that it honors your ways of knowing. There’s this phenomenon that we’ve noted which has been dubbed epistemicide. Epistemology is ways of, is knowing, knowledge. Epistemicide is the killing off of different ways of knowing. And to some extent if you think about our data systems or you think about how we are saying what is truthful and what is evident, it is dependent upon a certain quantified, average statistical, statistically significant set of information. That’s another really dangerous thing to critique I know because we don’t want ideological decisions, but I think in the same way as we need to think about a more evolved form of democracy, we also need to think about a more evolved form of how we determine truth and evidence.

Laura Flanders:

 

Is it possible? Because I’m thinking of the thinking that has been done about there are two kinds of people. There are people that like things fixed and there is a formal moral code and we get it written down and it’s put in the law books and then we follow those rules and we say this is true and that’s false. And then there are the kinds of people who believe things are culturally true or false depending on different circumstances that we live in created, changing realities. Are we capable of kind of shifting from one to the other?

Jutta Treviranus:

I think one of the issues is that we think in binaries all the time.

Laura Flanders:

 

Of course, there’s not only two kinds of people.

Jutta Treviranus:

The third dimension is that we live in a complex adaptive system. And so what we need to do is we need to not think about winning, not think about best, but think about the range. What is the range of needs? What are the range of choices that we have? And then to try to provide a spectrum. One of the things that, I critique technology a lot, but one of the things that our technical systems have allowed us to do is to create tools and environments where we can morph. If I had to design a door, I would have to design it in such a way that it anticipates everybody that might come to it. But if I were to design an entryway to an environment that is a digital environment, a networked environment, then I can have it change depending upon who’s there and I can call upon all the individuals that are connected to it to help with that change so I don’t have to do it redundantly.

Laura Flanders:

 

It is the climate activist Greta Thunberg changing anything in the sense that she being on the spectrum has said very articulate, she’s been very articulate about my special powers, you used to call inadequacies. But they are exactly what is enabling me to read this data, integrate this data, communicate it clearly and have a sense of urgency that you guys have lost.

Jutta Treviranus:

Exactly.

Laura Flanders:

 

Think she’s changing things?

Jutta Treviranus:

Yes. I think she’s changing things and I think there are many more Greta Thunbergs out there if we give them the time, the attention, the tools. It’s the individuals that are out there that are, that have to be resourceful, that have no choice, they can’t use the standard systems, they have to change things. And so it’s, they are sort of are, they can lead in the innovation, in detecting the things that we haven’t done.


Laura Flanders:

 

The outliers will get us out of here. Something like that.

Jutta Treviranus:

We hope, we hope.

Laura Flanders:

 

Jutta, thank you so much. Really a pleasure talking to you. Thanks for coming in.

Jutta Treviranus:

Thank you.

Laura Flanders:

 

You can find more information and links to the interviews that I mentioned and others on this topic at our website, that’s lauraflanders.org. Thanks.

 

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