Digital Methods Initiative Winter School 2018 at the University of Amsterdam. The idea is that rather than trying to take analogue methods and apply them them to the internet, we take tools that are only possible because of the unique characteristics of the web. It’s an intense week of experimentation, research, and learning, all rolled into one; if you’ve worked in hackerspaces and startups, the general vibe of the place will be familiar. It’s like an explosion of several tangential whirlwinds that kind of finds a way to coagulate and settle within a week. While my organisation/control-freak tendencies were a little overwhelmed, with that one week sprint we probably saved ourselves a good three months of work where we would have been faffing around. I would like to perhaps save you some of the save headaches, by making it very clear some of the key methodological points & assumptions. (You know how I like to clarify assumptions).What happens when you add one varied toolkit for digital methods, a research question, an enthusiastic team, the equivalent of a collective IV drip of caffeine, and pile them into a room for a week? Last week I had the pleasure of co-facilitating a project at the
ethical oversight of AI research, and rightly so. Problem is, ethical oversight hasn’t always stopped past research with questionable ethical compasses. Part of that, I argue, is that the ethical concerns raised largely by social scientists come from a completely different world view to those from a more technical background. While AI research is raising new problems, particularly with regards to correlation vs causation in research, but the tools we have to solve them haven’t changed that much. With this blog I want to question – can methodology help social and technical experts speak the same language? Since my masters degree I’ve been fascinated by the fact that people working in different disciplines or types of work will have completely different approaches to the same problem. Like in this article on flooding in Chennai, I found that ‘the answers’ to solving flooding all already existed on the ground, it’s just the variety of knowledges weren’t being integrated because of the different ways that they’re valued. I was recently speaking with my brilliant colleague and friend, who is a social constructivist scientist working in a very digital technology oriented academic department faculty. This orientation is important to note, because the methodology deployed for science and research there and the questions being asked are influenced to a large degree by the capacities and possibilities afforded by digital technologies and data. As a result, the space scientists see for answers can be very different. In reviewing student research proposals, she found she was struggling because some research hypotheses completely ignored the ethical implications of the proposed research. In talking it through, we realised that most of the problems arose from the assumptions that are made in framing those questions. To take a classic example, in the field of remote sensing to identify slums, it is relatively common to see that implicit assumption that what defines a slum is the area’s morphology, that that definition is by the city planners and not the residents, and how locals interpret the area or the boundaries of the neighbourhood may differ completely. The ethical problem, beyond epistemology, is what can then be done in terms of policy based on the answers that that research provides. To go back to that paper that caused the controversy about identifying people’s sexual orientation from profile pictures downloaded from a dating site. It’s based on a pre-natal hormone theory of sexual orientation, which is a massive assumption in and of itself. Even the responses to the article have basically boiled down to ‘AI can predict sexuality’, even though that’s blatant generalisation and doesn’t look at who was actually in the dataset (every single non-straight person? Only white people?). That, and then the fact that they basically ‘built the bomb to warn us of the dangers’, has a lot of assumptions about your view of ethics in the first place. Like my 10th grade history teacher used to say, to assume makes an ASS of U and ME. (Thanks Mr. Desmarais) More precisely, to assume without making the assumptions explicit. Not clearly articulating what your assumptions are is a *methodological* problem for empirical research, with ethical *implications*. Unexamined assumptions mean bad science. Confounding variables and all that. For reference, in statistics there is an entire elaborate, standardized system of dealing with assumptions by codifying them into different tests. You apply one statistical test, which has a particular name, because of the assumptions you have – i.e. I assume this data has a normal distribution. If you’re using mixed methods, it becomes much harder to have a coherent system to talk about assumptions because the questions that are asked may not yield data that is amenable to statistical analyses and therefore cannot be interpreted with statistical significance. All the all the more important here to make assumptions explicit so they can be discussed and scrutinized. Some ethical concerns can be dealt with more easily when we remember methodological scrutiny and transparency, bringing research back to the possibility of constructive criticism and not only fast publication potential. How this process is dealt with currently in academia is ethical review, hence the call for ‘ethical watchdogs’. Thing is, In terms of the process of doing science in academic settings, ethical review is often the final check before approval to carry out the research. When I did my BSC. In psychology, sending the proposal to the ethics review board felt like an annoyingly mandatory tick-box affair. The problem with this end-of-the-line ethical review is:There is a growing call for
- It’s not clear why the ethics is important to actually carry out the research
- If the ethics board declines, you’re essentially back to the drawing board and have to start again.