The five Futures of the University (Connected, Creative, Healthy, Radical and Responsible) are based on the principles that underpin our strategic plan and characterise the type of research and enterprise that we currently do and plan to expand. Academic leads are senior academics who provide thought leadership, working to consolidate our existing strengths and explore and develop new possibilities.
In this new role, I am keen to contribute to the development of the University’s interdisciplinary research environment that nurtures creativity and innovation. The opportunity to produce inspirational solutions and positive change has never been greater, and I am a strong supporter of bringing together the arts, science and technology to do this.
In 2021 Creative Futures will create an interdisciplinary art/science/technology hive of innovative thinking that promotes scientific understanding, and ignites applied collaborations. We will bring together technologists, scientists, makers, artists, practitioners who employ creative thinking in their projects, in various activities in the next year, in order to support interdisciplinarity and student engagement with research.
The COVID-19 crisis has released a large amount of data about infections and deaths worldwide, and understanding what these data mean is essential for influencing public behaviours, such as self-isolation and social distancing.
This is not just my view: it is shared by groups now active in the COVID-19 crisis such as the #data4covid19 initiative. The Data Stewards Network advocate for
BUILDING CAPACITY. They say:
“Governments should increase the readiness and the operational capacity and maturity of the public and private sectors to re-use and act on data, for example by investing in the training, education, and reskilling of policymakers and civil servants so as to better build and deploy data collaboratives. Building capacity also includes increasing the ability to ask and formulate questions that matter and that could be answered by data. Such a list of priority questions and metrics could facilitate more rapid response by critical data holders.”
From my point of view, as the project lead of the ART/DATA/HEALTH project, I also find it important to address other skills:
First,citizens need digital skills that help them to spot misinformation about the spread of the COVID-19 virus, which gets circulated online. The public needs to be able to tell what is credible information and what not.
Second, now that many of us are asked to work remotely, we are signing up to new teleconferencing tools – but there are quite a few data privacy concerns, raised by organisations such as the Electronic Frontier Foundation. How can we work and connect with friends and family remotely during COVID-19 while keeping our personal data safe?
It is hard to grasp the impact of the coronavirus on a local scale, especially when the threat seems “distant”, or affecting “others”. This difficulty is exasperated with the “keep calm” attitude, which has resulted to significant delays in implementing measures, especially here in the UK. How can data science help us understand the COVID-19 situation better?
VISUALISING KEY INFORMATION
One way in which data science is currently being used is to provide key information with simple visual and simulations. The Medium article written by Thomas Pueyo on 10th March 2020 (and updated) received 40 million views in a week and was translated in over 30 languages. The article contains tons of useful information and lots of graphs, which audiences will have got used to seeing in social media in the last month already. Pueyo made some data visualisations himself on the effect of travel restrictions, which shows clearly the decrease of transmission rates.
Another key way that data science is used however is for modelling the spread of the epidemic and to advice public health and officials on important decisions, for example on closing schools or research funding for a vaccine. For example by mid-January, one group of data scientists had circulated an analysis listing the top 15 cities at risk of the virus spreading, based on airplane flights and travel data (Greenfieldboyce 2020).
The Washington Post model visualisation that was shared extensively in social media as the key to understanding social distancing shows a simulation of people depicted as dots. It shows changes of count of the recoverd, healthy and sick over time, but interestingly it does not depicts deaths. (Stevens 14 March 2020)
Looking at simplified visualisations like this is useful, but we should be reminded that modelling is exactly that: modelling. It cannot provide accurate predictions; it can rather provide indications that might be useful for policy makers to get their head around potential future scenarios. This because the quality of available COVID-19 data is poor: “Right now the quality of the data is so uncertain that we don’t know how good the models are going to be in projecting this kind of outbreak,” says Marc Lipsitch, an epidemiologist at the Harvard T.H. Chan School of Public Health (Greenfieldboyce 2020).
In order for data science to be effective in informing and advising decision makers and citizens however, models and modeling tools, and data that underpin these decisions should be made openly public. This will allow both experts and citizens to scrutinize such decisions. As the Open Data Institute (ODI) CEO Jeni Tennison notes
“the models governments are using are more sophisticated than the Washington Post model. They are based on evidence about other epidemics, and data about this one. They might take into account factors like how long after infection people become contagious, when they start showing symptoms, and how long they are contagious after they recover; different levels of social mixing by different people; and people’s compliance with instructions.”
The #data4covid19 initiative has been developed to put pressure for more openly distributed data, so that these data can be used by scientists in a systematic and sustainable way during and post crisis. The initiative aims toward building data infrastructures that are key to being prepared to tackle pandemics and other dynamic societal & environmental threats in the future (TheGovLab 16 March 2020)
The group bring the example of how mobile phone data were used in the Ebola case, and how Facebook data were re-used to understand public perceptions around the Zika virus in Brazil, and so on.
A wealth of projects have responded to the call to build an infrastructure for data-driven pandemic response. These projects are listed to “show a commitment to privacy protection, data responsibility, and overall user well-being”.
“The number of people found to be infected with covid-19 doubles every 3 to 6 days. With a doubling rate of three days, that means the number of people found to be infected can increase 100 times in three weeks (it’s not actually quite this simple, but let’s not get distracted by technical details).”
The post also explains the difference between logistic and exponential growth.
“Logistic” growth refers to the “s-shaped” growth pattern of epidemic spread in practice. Obviously exponential growth can’t go on forever, since otherwise there would be more people infected than people in the world! Therefore, eventually, infection rates must always decreasing, resulting in an s-shaped (known as sigmoid) growth rate over time. However, the decreasing growth only occurs for a reason–it’s not magic. The main reasons are:
Massive and effective community response, or
Such a large percentage of people are infected that there’s fewer uninfected people to spread to.
Therefore, it makes no logical sense to rely on the logistic growth pattern as a way to “control” a pandemic.”
Note 2: One example of how this is being taken up is a modelling exercise, which provides graph visualisations for staying at ‘home’ households, and households that they categorise as ‘moving’.
The “home,” household “stays in their house, receives deliveries of food or other necessities, and practices social distancing (6+ feet) if they go for a walk outside. They make decisions like whether to order take-out, whether to treat Amazon or Instacart type deliveries with dilute bleach or let non-perishables with hard surfaces sit for 2 days, etc. They also decide whether to go see their “best friend” once every 10 days.” The Moving household A “moving” household is a household where one or more people in the household have a job where they move around in the community. This includes people who are delivering food, bagging or boxing food in distribution centers, police, firemen, doctors, nurses, grocery store workers, and so forth.
A video recording of the talk “Understanding data power from a feminist perspective”, which I gave at the 3rd International DATA POWER Conference global in/securities, an be accessed here. (hosted by the ZeMKI, Centre for Media, Communication and Information Research, University of Bremen in cooperation with the Universities of Carleton, Canada, and Sheffield, UK, 12-13 September 2019)
You can read the relevant chapter in Fotopoulou, A. (2019). Understanding citizen data practices from a feminist perspective: embodiment and the ethics of care. In Stephansen, H. and Trere, E. (eds) Citizen Media and Practice.Taylor & Francis/Routledge: Oxford. See Google Books here
An updated written version will appear in my forthcoming book Fotopoulou, A. Forthcoming.Feminist Data Studies: big data, critique and social justice. SAGE Publications.
This theoretical paper introduces how the notion of “care”, as developed in feminist science and technology studies (de la Bellacasa 2011), can be a productive analytical and critical approach when scrutinizing the manifestation of power relations in data practices. The matters of power and the politics of data have far reaching implications for the politics of the everyday. The paper argues that approaching such political issues in data practices as “matters of care” allows us to account for their affective, embodied and material elements, including the habitually devalued human labour of data users, activists, producers, consumers and citizens. Outlining the differences between justice (Dencik et al. 2016, Taylor 2017) and ethics approaches to data power, it is further shown that, guided by the question “Why do we care?”, the notion of care inserts particularity and empathy in social justice frameworks. The paper provides examples of areas of application of an approach to data power guided by feminist politics of care, alongside issues of data governance, regulating the data-driven economy and data privacy laws. In this way the paper maps a theoretical roadmap of feminist data studies and practice theory, which is focused on materiality and embodiment and is committed to unsettling the power relation of race, class, gender and ability in datafied worlds.
de la Bellacasa, M.P., 2011. Matters of care in technoscience: Assembling neglected things. Social studies of science, 41(1), pp.85-106.
Dencik, L., Hintz, A. and Cable, J., 2016. Towards data justice? The ambiguity of anti-surveillance resistance in political activism. Big Data & Society, 3(2), p.2053951716679678.
Taylor, L., 2017. What is data justice? The case for connecting digital rights and freedoms globally. Big Data & Society, 4(2), p.2053951717736335.
Our panel “Feminist approaches to data practices” has been accepted at the 3rd International DATA POWER Conference global in/securities, hosted by the ZeMKI, Centre for Media, Communication and Information Research, University of Bremen in cooperation with the Universities of Carleton (Canada) and Sheffield (UK), 12-13 September 2019.
Here is the line-up of papers, in alphabetic order, and the panel rationale.
Lina Dencik: “Situating data (justice) in critical social theory”
Aristea Fotopoulou: “Understanding data power from a feminist perspective: embodiment and the politics of care”
Stefania Milan: “What feminist theory of datafication emerges from contemporary data activism?”
Data-based systems and technologies pose pressing issues in relation to social justice, and there is great need for focused and explicit critiques that addresses intersecting structural inequalities such as gender, race, ability and sexuality. Embarking from conceptualisations of data practice, this panel explores how feminist theoretical, methodological and praxis approaches can help us understand the structures of power and privilege is datafied worlds.
My new research project is about to start (once a Research Assistant joins me: see job ad here). I have become very interested lately in what can constitute the principles of a critical data literacy that is central for citizen engagement. Big data are everywhere, and they are transforming the way we live. But making sense of data and communicating in ways that are relevant to broad audiences and for the social good requires the skills and literacy to access, analyse and interpret them. My new University of Brighton research project addresses the need to develop practices that allow citizens to work with data, to make data more relevant and appealing to communities, and enable their engagement in policy debates. Instead then of focusing on enhancing data analysis and technical skills, I am interested to explore how a combination of creative media, storytelling and analytics allows participants to generate debates around specific issues that affect their communities.
I will be working with community organisations in the Brighton area, running a Datahub workshop focusing on sexuality/gender as they play out with other social issues, such as poverty, unemployment and housing. For updates see https://criticaldataliteracy.com.