Here four UTS researchers - two from STEM fields and two from HASS fields - discuss the research projects they have worked on together and the many ways their differing expertise has combined to deliver outcomes that are bigger than the sum of their parts.
Professor Fang Chen (FEIT) and Associate Professor Heather Ford (FASS) discuss their work on uncertainty
"When we tried to find the research goal joint research goal together, it wasn't about you convincing me or me convince you. It was about how we can be jointly and complementary to each other, to fill the gaps each other has."
Do you remember how we got to working on the uncertainty project?
Oh absolutely. I remember that's an exciting time when I was chatting through to the faculty about you know what sort of social aspects around ethics and of course your name popped up and particularly around communications. And then, you know, there we go we sit together and talk about what are the points and the uncertainty not too far out just to pop up.
I remember starting off by talking about uncertainty and you know I'd heard this term before and obviously it's very connected to transparency. But it was really interesting because I learned there's this whole field that you are embedded in around communicating uncertainty. And I think we started off the project with this common term that we both come at in very different ways. But that was really helpful and interesting to me.
So from the AI machine learning model, one of the biggest challenges is because it's probability based hence you have uncertainty inherited in the in the model. Of course, inherited in the data as well right. This probability is clearly linked to the risk to decision making and the missing part is coming from human side of interpretation. To interpret those ones right, how to interpret the uncertainty, not to come from purely data science and the modelling perspective
Do you want to talk a little bit more about some of the things that we were doing?
Yeah so I remember we collected 70 to 80 papers partially from data science and partially from social science. Even to look at the word cloud of the key words that pop-up from those papers, it looks so different. So from uncertainty data science perspective, you look at the models you look at the data look at the parameters, on the other side you look at the risk you look at communications, you look at you know very different terms. So i'm so uh nicely how can you say shocked by the difference but that is the first discovery to me that we're coming from very different angle and so uh glad that we sit on the same page after that.
What really struck me was that data science because you're dealing with these really complex models and trying to really predict with as much certainty as possible what the level of uncertainty is. You're really looking within the model, within the system. As social scientists we what we do is we kind of look at everything outside right. So we're looking at, you know, different kinds of people that come at the system with different kinds of belief systems. We're looking at the political economy of how these institutions work, the kind of trust that people have in those systems. So all of these like external things and so it's a really nice marriage trying to think about really trying to model uncertainty really accurately, and thinking about all the different ways that you can do that, but then then also thinking about how ordinary people or specific types of people depending on the system would come at that uncertainty. How would they interpret it and what might be the problems in their interpretation and all those kinds of factors over time.
How do you find about working together with the people coming from our group or different disciplines?
Well it's an amazing opportunity isn't it because when do you get this opportunity. You hardly ever do. I mean there's not many times when you're able to really hear people's perspectives especially someone who's a sociologist of technology right. I mean I study technology and I'm an ethnographer and I tend to work with the groups like Wikipedia as one of my main sites. So I work with technologies involved in Wikipedia but in the university I really haven’t had that opportunity before. So it's really really great to hear data scientists perspectives on all of these things. Generally interdisciplinary work takes a lot more time right?
Absolutely I think I agree with that a challenging starting time. Because we needed to basically step outside of the comfort zone to read different things right. I have to admit that I haven't read so many of the papers from social science perspective. I feel like I can close my eyes and read all those data science papers very quickly, I just flip. But one of the papers coming from your literature and to me that probably takes three four times than the usual time reading per paper. The second one is the terminology. I think that the terminology is of course that's the foundation of the challenge coming from interdisciplinary cross-disciplinary because we use different terminologies. How our you know professional life and try to even think from others perspective that's a challenge.
Were there any things that you thought worked really well when we were working together?
When we tried to find the research goal joint research goal together, it's not about you convincing me or I convince you. It was about how we find you know how we can be jointly and complementary to each other, to fill the gaps each other's have so i think that works well.
Thanks for joining me today Fang. I love talking to you and we don't have that many times to do it.
Thank you my pleasure and look forward to working with you more in future.
Dr Jonathan Marshall (FASS) and Associate Professor Kaveh Khalilpour (FEIT) discuss their Narrabri project
"We know that climate change is the big elephant. We know that we need to put the forces together to address that. But sometimes we are touching the elephant from different angles and we don't recognize that we are on the same topic."
The point of the project was to investigate a renewable energy startup in Narrabri. People call themselves Genie Energy and they're sort of dedicated to bringing community renewable energy to Narrabri in the hope of freeing the town from gas and coal, and also starting up new manufacturing and other business there. And we also want to write a computer model that actually describes how community energy transitions occur, develop that across Australia and also into Europe, so that we end up with a computer program that people can actually use to get recommendations about how they should actually work towards community energy and its primary benefits.
Absolutely. The beauty of this collaboration John, in my opinion, is that just before doing this project together we had done a few projects on community energy development. But in most of those projects what we had done, the society the community was not involved in our research. So we had input from the community, we were just based on our imaginations, we were doing our technical model, optimization data analytics tools and everything. We were producing results but without knowing how the community is going to use it. But this project brought things together. Very often we are missing the social parameters. Look in a way, for getting information from the community you need to develop some sort of report with the community, some sort of human touch, human communications, all those things. But very often we fail to be honest, it's difficult for everybody. But working with you and also your team in a way I could easily see that that missing element where we’re in a way connected yeah the society that we were approaching like our December visit to Narrabri, it was totally different from the other visits that I was used to. In a way you brought the community with a different spirit.
I find that social scientists tend to cheat treat technology as kind of endlessly flexible, without the actual constraints of how the thing works and it's really useful to be with people who understand how renewable energy works. Also i mean in the long term it's also useful because you know again it's really hard to test your theories in the social sciences, but if you build a computer model out of it you can actually see whether what you're getting works on the ground and whether in fact it actually replicates the sort of things that you've seen on the ground. I find that really exciting so.
Someone was joking that Berlin Wall fell 30 years ago but still these cross disciplinary walls exist in universities. Now renewable energy, climate change the challenge, even i'm talking even from engineering perspective, just within engineering we have electrical engineers, we have computer scientists, uh different background. Sometimes even between engineering school we don't collaborate enough. To address the same problems, it needs a cross-disciplinary so coming to like cross-faculty kind of thing. The same problem exists. The big elephant is there. We know that climate change is the big elephant. We know that we need to put the forces together to address that. But sometimes we are touching the elephant from different angles and we don't recognize that we are on the same topic. So modelling from engineering perspective or from your perspective.
I agree. I mean one of the reason that if you go back far enough that all of this started was we had a visiting scholar from india who came out Gopal Saranghi. He just wanted to talk to everybody in UTS who knew anything about renewable energy. Because I was sort of looking after him I suddenly found all these people in the university who I'd never heard of who are doing stuff on climate change and renewable energy. That's how we got to meet Alexi. Simply because he just didn't take our barriers seriously and that's had a major effect on subsequent life I guess.
Jonathan, what are your tips for future collaborations, to those colleagues who want to start this kind of cross faculty collaborations?
I guess first of all find your people. I mean that's the number one problem. When I started my last fellowship, I talked to a lot of people but everybody was very cliquey in their groups. It was really difficult to make contact of any sort really whatsoever that got out of that group. So I think the sort of thing that that Gopal did, which was basically ring people up and say hi you know I'm somebody from this faculty and I'm doing this kind of work and I would like to talk to you about your work and see if we could set anything up. Which is really really important and you just don't know who the people that you're going to end up working with.
John, I have fully enjoyed working with you over the last year and I hope that this will continue infinite.
So do I, it's been really great fun and real expansion of understanding and I don't think any researcher can ask for more than that. So yeah, I hope it goes forever too.
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