by Erland Howden
As a structure for the brainstorming exercise discussed in post 8, we divided our concepts into the different fields of emergent practices – service design, generative design and data visualisation. This was helpful in stimulating further ideas, by pushing us to consider alternative ways of approaching the problem space we had sketched for our issue area – asylum seekers and refugees. However, it also showed that there were some blurry lines between these fields, with some of the ideas we talked about potentially fitting into more than one category.
Another tool we used to guide the discussion was to carry forward a system we created during our collaborative research phase. When sharing information, we created a system of hashtags related to each group member’s areas of focus, that would identify the key aspect of the issue to which a particular source pertained. For example, we used #resettlement, #attitudes and #mentalhealth. As we got into the problem space definition, we then brought these categories – along with the relative expertise that each person had built in their focus areas – into defining key aspects of our issues’ problem. We also brought to bear the findings and reflections from the recent issue mapping exercises that identified key polemics within our issue.
The key strength of the brainstorming exercise was that we were able to generate a wider latitude of concepts and interrogate them to immediately make them sharper and more relevant by discussing them with a small group, rather than ideating individually. At the same time, working with just a few people was likely more productive than trying to collect and document an ideation process with a large group of 10, 20 or more. On the other hand, despite a large body of research between our group members collectively, not a lot of the research we had each done focused on recent solutions, campaigns or tools and I think we faced a weakness in potentially replicating existing projects and not being able to apply learnings from a deep analysis of existing design responses.
This mind map looks at areas of interest to address within my problem statement. The map is divided into two broad issues, to help narrow down my focus. While the issue of national security was intriguing to me, I ultimately decided to focus on ownership of information as it was a much more universal issue. I made this map before generating problem statements to help summarise my view of the topic, and ensure that my statements were relevant to the issues I wanted to pursue.
This less well organised map shows some of the problem statements I generated before choosing and refining. In generating problem statements I tried to be as specific in situating the issue; which was difficult to do while keeping the phrasing succinct. Moreover, I was also careful to not make the statement so specific that I would have trouble generating design proposals in response to it. The statement I chose to refine from this exercise was “users are disconnected from the information they provide to online services”, which was later modified to “users are disconnected from the personal information they provide, either willingly, or unwillingly, to online services”. Adding in the phrase about willingly or unwillingly providing data addresses not only data collection programs, but also the data we agree to exchange when we register for online services.
Finally, this brainstorm looks at possible design solutions in response to my problem statement. Created in collaboration, with Brain, Chloe and Collette, this map encompasses both solutions, and broader ideas about what the proposal should accomplish. Despite choosing a relatively open statement, it was still very challenging to generate ideas. This is something that I think disproportionately affected the online privacy groups, as it is an extremely technical topic. Instead of focusing on mind mapping stakeholders and their emotions over the past weeks it would have instead been useful to undertake secondary research on the topic. I think that we spent too much time going over the same information in class and missed out on an opportunity to gain an understanding of the extremely complex mechanisms behind data surveillance, which in turn has made it hard to propose design solutions.