Do We Visualize in Order to Hide? By Laia Blasco-Soplon
One of the first advocates of the potential of visual thinking was Rudolf Arnheim, who argued that visual perception is what allows us to truly understand experience, and believed that form is inseparable from content (Arnheim, 1969).
Also before the mass internet access, Richard Saul Wurman predicted that the explosion of available information would be so great that it would require the intervention of professionals dedicated to organising it, making it meaningful, and presenting it in a coherent, systematic, understandable way (Wurman, 1989). In a sense, he was forewarning us of the need that has been growing in recent years, to give shape to this enormous amount of information, to visualize it.
In 1993, George G. Robertson and his colleagues (Robertson et al., 1993) defined data visualization as a mix of different aspects of images, graphics, scientific visualization and human-computer and human-computer interaction, as well as information technologies. From this first definition up until the present, visualization has come to include many different fields: research, journalism, illustration, education, art, design, and so on. And it has been used as a tool for organisation, cognition, and also expression.
Data visualization is becoming more than just a series of tools, technologies, and techniques for managing large data sets. It is becoming a medium in itself, with many expressive possibilities (Rodenbeck, 2008). It is ready to become a mass medium (Viégas and Wattenberg, 2010), and is already starting to be one of the main tools and means for graphic user interface design and development.
There is little doubt that quantifying, processing, and visualizing data are valuable operations in an increasingly datified world. And this is precisely why we need to become more aware that interfaces that use data visualization to create user graphics are based on the same mechanisms as other user interfaces.
Most cultural activity, past and present, is filtered through the interfaces by which humans communicate. Interfaces act like code that carries cultural messages in a wide range of media. And like any code, interfaces are never simply neutral data transmitters; they affect the actual messages, contributing a model of the world, a logical and ideological framework (Manovich, 2001). Interfaces mediate between humans and machines, between machines and machines, and between humans and humans. As such, interfaces involve exchange between data and culture. As a cultural paradigm, interfaces do not simply affect our creative output or the way we present the world, but also the way we perceive it (Andersen, 2011).
Interfaces are cultural tools or artificial devices that use metaphor, just like languages and communication do. When these metaphors are well designed they become naturalized and imperceptible, but they are never really transparent, even when they appear to be invisible (Scolari, 2004). These metaphors do not only influence our idea of what our computers can and cannot do, they also affect speech, discourse, and action in a broader sense. They shape and guide public debates, academic discourses, technological innovations, and the ideological positions of the individual (Boomen, 2014). They don’t just speak of their own operation, they also seep into social life and raise questions that can only be answered through a political interpretation (Galloway, 2012).
Interfaces – including those that use data visualisation – are affected and defined by technological, economic, ideological, cultural, historical, and political matters that condition their models of representation, and also the way they are perceived.
File:Hidden visible-05.png Fig. Diagram that illustrates what the interface shows and what it hides
It has always been difficult to reveal the mechanisms that conceal the political dimension of interfaces, but interfaces that use data visualization introduce two extra obstacles that further strengthen the sense of false transparency of the interface.
The apparent neutrality of data
“The data say, the data reveal, the figures show... If there’s data, it must be true!” The fact that a visualization is based on data does not mean that it necessarily presents all available data on the topic. It is impossible to present “all” data. A visualization uses the data that has been found trough data mining or compiled on a database, the data that was obtainable, the data that was intentionally chosen, the data that somebody has decided to show and present... and also the data that the user has been able to navigate, handle, and see. And in any case, who said that everything could be explained through data? Not everything is quantifiable, a large part of reality escapes data, but remains just as real.
We need to overcome the fallacy of data neutrality. Data are always partial, interpreted, and interpretable, and as such they are never neutral or objective.
Our fascination with self-portraits
“Hey! That’s me! That’s me over there!”
Given the constant flow of data we produce daily as we browse the web, use localization devices, or, increasingly, run self-tracking apps (running, shared routes, nutrition, etc.), “data portraits” (Donath, 2010) are being studied and discussed beyond the art and academic field, including blogs and websites. One of the first examples of this growing tendency towards the “quantified self” (O’Connor, 2013) was the work of the designer Nicholas Felton, who has been analysing and visualising his day-to-day activity in his Personal Annual Report, creating data self-portraits that he has published each year since 2005. Felton is also the founder of Daytum, a website with information on self-tracking. Mass datification has led to the emergence of all kinds of apps for representing personal information. Who doesn’t enjoy looking at themselves in the mirror if the image reflected back is attractive? It is fascinating to identify oneself among data, there are so many people represented! Visualizations thus have an aura of mysticism and heavenly truth: “I could be there! That’s me!”
We need to overcome our fascination with self-portraits and replace it with a critical approach that questions: what data does the visualization show? what doesn’t it show? in whose interests are the data? where are the data stored? who handles the data? who do my data belong to? how are they represented? who do they represent? Every day, we interact with interfaces that are presented to us as seemingly neutral and transparent, but are actually full of underlying political tensions. Just as we adapt ourselves to them, their ethics and aesthetics shape us, and become creators of realities that have agency and diligence. Interfaces based on visualization strengthen this sense of false transparency that even further hides the political mechanisms that organise them. As users, do we want to simply remain fascinated and passive observers our datified portraits? O do we want to become aware of the political dimension of interfaces? And as designers, do we want to visualize in order to hide? Or do we want to design interfaces and visualizations from an ethical perspective that explicitly draws attention to their cultural, ideological, and political dimensions? Can we make visible that which really is invisible?
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