“It definitely felt strange going overseas, but it was intellectually invigorating.”
Cybernetics PhD student Ned Cooper travelled overseas in April for the first time since the pandemic began. His destination: New Orleans, Louisiana in the US where he presented a paper at the prestigious ACM CHI Conference on Human Factors in Computing Systems held from April 30 to May 5. It was a trip of many firsts. It was Cooper’s first time presenting at CHI—a huge deal for early-career researchers in the field—and his first time visiting New Orleans, where he devoured a few platefuls of local delicacies — Gumbo, Crawfish Etouffee, Beignets, Jambalaya, Oysters Bienville, and Po’boys.
Cooper is in the first year of his PhD studies, which he pursued after completing his Master of Applied Cybernetics in 2021. He explores ‘participation’ in the development of AI-enabled systems. Before pursuing a PhD, he worked in strategy, legal and policy roles.
We caught up with Cooper to talk about the conference, his research and professional experiences.
Editor: Tell us the story of how you ended up on this career path. Is this something you have always dreamt of doing?
Cooper:
Over the last five years, I have become interested in socio-technical systems. While I am fascinated by and enjoy data science and computing research, I am increasingly conscious of the social questions that are central to decisions related to technology.
For example, I used to work at NBN Co, because I wanted to connect more people from the bush to broadband. Part of my job was identifying areas covered by one broadband technology that might be upgraded to another technology. This was a value-laden choice, despite the fact the task was often framed as merely identifying the cheapest and quickest areas to upgrade. We used data science techniques, including machine learning, to identify areas to be upgraded, but those techniques did not always incorporate all the questions that are relevant to such a decision. For example, who uses broadband technology the most? Who might use broadband more if they had an upgrade? Who could benefit the most from an upgrade? What else did we need to do, other than upgrading technology, to help people get the most out of broadband? And, as always, who should make all of those determinations? These are social questions, not only data and technology questions.
You have a fascinating professional journey, having worked in strategy, policy, and law reform across some industries. What prompted you to focus on research in this field?
While I was working as a lawyer and policy officer at the Aboriginal Legal Service (NSW/ACT) I read ProPublica’s story about COMPAS, a criminal risk assessment system. My work at the time included advocating for bail law reform, and I was interested (and concerned) about the potential for automated risk assessments in Australia. After reading that story, I started following research developments in and applications of machine learning more closely.
Tell us about how you discovered the School. How was your experience in the master program and did that play a part in your decision to pursue a PhD?
After I read about the COMPAS story I looked around for schools researching machine learning. I came across 3Ai in 2017 and followed the institute for a couple of years, while I built up my skills in data science and computing, before applying in 2019. During the master’s program, I enjoyed engaging with scholars and academics at the institute from a wide range of backgrounds— not only disciplinary backgrounds but also personal backgrounds. In terms of content, I enjoyed the focus on systems and complexity—topics I have always been interested in, and aware of, but not sure how to study or put to use. For example, at law school, I was less interested in individual cases than my fellow law students and more interested in the points of failure in social and economic systems that result in someone ending up in court.
Tell us about the research you presented at the ACM CHI Conference.
We looked at the past two decades of published work about computing researchers working with community groups to develop technology. Through that review, we came to understand the themes of community-collaborative computing work. We also identified some areas where we think computing researchers need to pay attention to ensure future community-collaborative computing is beneficial for both researchers and communities.
What is the inspiration behind the research? Talk us through the collaborations that happened between the researchers involved.
I started working with the Google People + AI Research team during my capstone project for my master’s. When I started my PhD, I continued working with the team (plus Professor Gillian Hayes from UC Irvine) to research and draft the article for CHI.
While I have a background in community legal and policy roles, when I started the capstone project, I didn’t have experience in community work in the context of computing. At the same time, my co-authors from Google were starting to undertake more work in this space. All of us wanted to understand how to do this type of work responsibly and what, if anything, was unique about this type of work in the context of computing.
Prior to the conference, I had not met any of my co-authors in person, after many meetings online. CHI was the first time. I am not just a bobblehead floating in the videoconference ether!
How does your research subject intersect with cybernetics?
Human-computer interaction (HCI) shares some features with cybernetics—HCI is multidisciplinary, and the concepts of control and communication that are central to cybernetics are important for many sub-fields of HCI. Most importantly, both HCI and cybernetics consider humans and human behaviour as key elements of technical systems. Our paper encourages computing researchers to consider humans not only as individuals but also in collectives or groups represented by communities.
Was there a question or provocation after your presentation that surprised you the most?
I was happy with my presentation and with the connections I made with the people who attended. Several questions and comments on my presentation related to the time it takes for researchers to do collaborative work with community groups, because of all the relationship-building work that goes into doing collaborative work. In contrast, computing research moves quickly and there is a relentless focus on publication. There is a tension between the ‘slow’ work of relationship building and the ‘fast’ work of experimentation, iteration and publication that categorises computing research. I am planning to work with a couple of people I connected with at the conference on how we might address that tension.
Any takeaways/realisations from your participation in the conference?
My two main takeaways from the conference are:
- How intellectually invigorating it can be to meet in-person with other researchers to discuss and debate research; and
- How physical draining in-person networking is after a couple of years of only connecting with people online!
What do you think is the biggest contribution of your research to the academic discourse and the society?
We need to develop approaches for engaging with communities of users in the technology development process, not only individuals. Groups of users are often more powerful than any one individual, which can level the playing field a bit between researchers and participants in research.