Imagining the Digital Society – Metaphors from the Past and Present
The current rapid social and technological change is giving rise to enormous uncertainties – and a great need for explanations and sense-making. How do we understand the digital society? When we talk about the future that we cannot know and the present that we do not understand, we cannot but use the conceptual apparatus of the past – with normative, social and economic implications. This article is introducing a series of articles on the politics of metaphors in the Digital Society. It aims to uncover the hidden assumptions and concepts within our discourses of the digital, piece by piece. The series is edited by HIIG researcher Christian Katzenbach and Stefan Larsson from Lund University Internet Institute.
Digital Transformations – and the Need for Sense-Making
We are living in a time of transformation. The digitalisation of nearly every aspect of contemporary society is bringing about profound changes in politics, economics, culture, and our everyday life. How can democracy be organised in the digital context? What are the implications of widespread automation and artificial intelligence for businesses and whole economies? What role do major internet companies play in organising and curating communication and information? The current rapid social and technological change is giving rise to enormous uncertainties – and a great need for explanations and sense-making.
When we talk about the future, we cannot but talk in terms of the past and the present. Imagining the future is always mobilising the past. Hence, it is no surprise that we routinely use existing concepts and well-known phenomena to describe emerging things and developments, leading to a conceptual path dependence, of sorts: Should we understand Uber as a taxi company, an employer or merely a software developer? Should Facebook be understood as an algorithmically dependent platform, or as a publishing house that is liable for what it publishes? Was the file sharing site the Pirate Bay to be regarded as an infrastructure, a storage facility or a bulletin board? This is not merely playing with words; existing notions bear normative assumptions and create regulatory implications.
Emerging phenomena typically lack a name, so we apply existing words to a new thing, although they might technically not be applicable. But metaphors, as George Lakoff famously put it, are not merely figures of speech, they are figures of thought. In consequence, by talking about the ongoing transformations using the terms of the past, we are also making sense of the present future and the changes that come about with the conceptual apparatus of the past, with normative, social and economic implications.
An Article Series on the Politics of Metaphors
Against this background, it is obvious why talking about the digital society and the ongoing transformations in politics, economics and culture is pervaded by metaphors. Indeed, metaphors such as cloud, platform, and big data are already so much part of the current discourse that they are barely recognizable as such. In the early days of the internet, Information Superhighway or the World Wide Web itself were dominant notions to describe the emerging infrastructure.
The aim of this article series is to learn something about the currently evolving digital society by unlocking the metaphors we apply. Our assumption is that this will shed light on the future that we cannot know – and even the present that we do not understand. And as metaphors are not merely words, this is a genuinely political process. Every notion, every metaphor is loaded: It provides a frame of understanding and of evaluating a new phenomenon – but in many cases, we could just as easily use different notions, which in turn might be contested by competing frames and metaphors. In that way, our discourse on the digital society is contingent – it could be different. The copyright discourses have provided ample examples of this discursive struggle: piracy and stealing have strongly dominated the discourse on copyright reform, yet digital copying could easily be termed differently, with vast political and regulatory implications. But what are the less obvious implications that metaphors like platform, cloud and big data entail?
In the coming weeks and months, we will be uncovering the hidden assumptions and concepts within our discourses of the digital, piece by piece. The series begins with an essay on Artificial Intelligence as a metaphor (or not?) by Christian Djeffal, followed next week by a piece on Revolution by Noam Tirosh and Amit Schejter. Over the course of the summer, you can expect articles on Sharing by Nicholas John, Platforms by Tarleton Gillespie and much more. This is even more important since by imagining the digital society, we are also shaping it.
If you are interested in submitting a piece yourself, send us an email with your suggestions.
This post represents the view of the author and does not necessarily represent the view of the institute itself. For more information about the topics of these articles and associated research projects, please contact firstname.lastname@example.org.
Sign up for HIIG's Monthly Digest
and receive our latest blog articles.
Whether civil society, politics or science – everyone seems to agree that the New Twenties will be characterised by digitalisation. But what about the tension of digital ethics? How do we create a digital transformation involving society as a whole, including people who either do not have the financial means or the necessary know-how to benefit from digitalisation? And what do these comprehensive changes in our actions mean for democracy? In this dossier we want to address these questions and offer food for thought on how we can use digitalisation for the common good.
Sustainable AI is becoming increasingly important. But how sustainable are AI models really?
Why is Artificial Intelligence so commonly depicted as a machine with a human brain? This article shows why one misleading metaphor became so prevalent.
Barriers in our physical environment are still widespread. While AI systems could eventually support detecting them, it first needs open training data. Here we provide a dataset for detecting steps...