Shaping the Digital Space
If the physical space is made of atoms, the digital space is built from data
Whether you are a casual user, student, or professional, understanding the inner working of how online platforms and technology is designed has become essential in our increasingly digitised world. The more you can understand it, the more you can make informed decisions about your online presence and who benefits from your data.
Over the past century, the world has undergone a powerful transformation: digitisation. This means that analogue technologies, material documents and even social interactions have been converted into digital form. Viewed through a linguistic lens, digitisation is very similar to a translation process: converting elements of the material world into a language computers can understand – data.
All our clicks, messages, uploads and digital behaviours contribute to a growing sea of data that become the raw material of the digital world. This data is structured, processed and connected to form what we first called ‘the web’, and now experience as ‘the digital space’.
Let us bring this to life. When you create a social media account, you input personal data: name, date of birth, location. Every interaction that you will then have from this digital profile: likes, comments, follows, combined to your digital behaviours (search, online purchases, AI use) make up your digital identity. This identity, which scholars refer to as a ‘digital twin’ (Kritzinger et al, 2018), acts as a data-made mirror of your online self. It reflects you and your life as captured by the data that you generate or that may be generated about you. While it is not a perfect reflection, your twin follows, evolves and shapes your digital identity.
We inhabit the digital space in ways that are both alike and different from how we inhabit physical space: we communicate, purchase, read and write. Consequently, with such a wide and transformative process as digitisation, understanding how it works is becoming an essential tool to read global processes.
A world of data
From Turing’s apple to algorithms: how we shaped and built the digital space
Unlike the physical world, the digital space is human-made: designed with intent, shaped by specific values, with goals and governed by invisible rules. From early innovations by Alan Turing and others, the digital world was built for efficiency: fast communication, accessible information, seamless banking and social connectivity.
With efficiency came commercial opportunity. Today’s digital infrastructure is not just a public utility; it is an immensely profitable industry. Free access does not mean that the platform behind it is not profitable. If you think about a search engine like Google, although it is free to search, the system is not a neutral, open archive of knowledge. Its algorithm prioritises content that benefits paying advertisers.
As digital scholar Safiya Noble argues, search results reflect commercial interests and cultural biases. When you search ‘backpack’, you are unlikely to find educational content about backpacks, the definition of a backpack, or how to make a backpack, you are more likely to see products from companies that paid to be listed at the top.
“Algorithms are not objective; they reflect the ideologies of those who create them.”
Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.
As it is profitable, it is essential to remember that the more efficient the digital space, the more valuable are technologies that allow access to it: from smartphones to software. This example is a good epitome of a critical principle of the digital space: free does not mean neutral. Most of the digital space is governed by economic logic as much as technical design.
The New Digital Era: Generative Technologies
While user generated data has formed the foundational building blocks of the digital space, we are now witnessing a new shift with the rise of machine learning and generative AI. More and more online content is no longer made by humans.
A recent Harvard Business Review article predicts that by 2026, up to 90% of digital content could be generated by artificial intelligence (HBR, 2024). What this means is that machines trained on immense datasets from the web can mimic human communication, tones and social patterns. From the 26th of May 2025, EU users’ data of Instagram, Facebook and other Meta platforms, like WhatsApp is being used to train Meta’s AI, which triggers privacy concerns for Meta users.
“In the EU, we will soon begin training our AI models on the interactions that people have with AI at Meta, as well as public content shared by adults on Meta Products.” Meta Platforms Inc., 2025. Meta Privacy Policy Update
When you use generative AI, you are interacting with a system that does not just respond to your words, but is designed and trained to understand your intentions, preferences, and even mood if you use it regularly. Generative artificial intelligence adapts to you, and this adaptation is only possible because of the large amounts of data already available, much of it generated by users like us.
While the digital space began as the result of a numerisation or digitisation of the physical world, it is now becoming a self-generating space, shaped by algorithms that few understand and fewer control.
Makers and Shapers
If we all contribute to building the digital space by generating or sharing data, not all contributions are equal and not everyone can influence what the digital space looks like.
“The digital divide is now the new face of inequality” UN Secretary-General’s Roadmap for Digital Cooperation, 2020
The first key challenge that generates digital inequalities is access. Despite more than 6.8 billion people owing smartphones worldwide, according to Statistica (2023), access to the digital world remains deeply unequal; this is what we often call the digital divide. This divide highlights the inequalities in access to devices, internet but also digital literacy and skills. As Jan A.G.M. Van Dijk (2005) argues, the digital divide is not only about access to devices or internet, but also about the skills, motivation and support needed to use digital tools meaningfully. These latter are essentially what makes the difference between what I call ‘digital participants’ and ‘digital shapers’. On one hand people who participate in generating data: general users like most of us. On the other hand: digital shapers or architects: people with the skills to influence the digital space with their coding, innovation skills.
“Only 40% of adults globally have basic ICT (Information and Communication Technology) skills, such as copying or moving a file”. UNESCO, 2019. Digital Literacy Global Framework
Now let us consider the following: In Europe, one of the most digitally developed regions, only 17% of individuals report any ability to code, which is the skill that allows the creation of websites, apps, software and platforms (DESI, 2022). This shows how small the group of digital architects really is, even in high tech contexts. Consequently, a very small minority of people can shape how data is delivered: what information is prioritised, who sees it and for what purpose. Essentially, if we follow the linguistic metaphor: most of us are navigating the digital world like we would navigate a region which we barely speak the language of.
Yet we rarely hesitate to share our personal information, elements of our life online, without having agency over what happens to them. That means that the power to shape the digital world is concentrated in a small, highly specialised group, with access to a large pool of all type of data.
Within ‘digital shapers’, the issue of representation is staggering. For example, World Economic Forum highlights in 2023 that women only represent 28% of the world’s tech workforce. Thinking back to generative technologies, OECD AI Policy Observatory (2022) reminds us that over 80% of AI research and development happens in 10 European and North American countries, showing that digital inequalities are very present demographically and spatially. It means that the digital space is shaped with a limited frame of cultural reference by a group that does not represent the socio-economic interests of most of the global population.
“Digital dividends are unevenly distributed. For digital technologies to benefit everyone everywhere, affordable access to high-speed internet is necessary” World Bank, 2016. World Development Report 2016: Digital Dividends. Washington, DC: World Bank.
In a digital space governed by a profitable economic logic and mainly managed by a minority of people, there are considerable inequalities in terms of representation and industry benefits worldwide.
Can we shape the digital space differently?
In the digital space we build and inhabit, what values do we want encoded?
We can start thinking more critically about our digital practices: what data do we share? What platforms do we support? How can we gain more agency or encourage ‘shapers’ from under-represented groups to gain power?
More broadly, creating a more inclusive and accessible digital space means advocating for greater transparency, wider access to digital education and more diversity among the people who design and shape the space we interact with daily.
The digital world is built from data, but choices of how it is used, and who gets to decide, remain inherently human.
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Zoe Waterlot
Quoted here:
European Commission. (2022). Digital Economy and Society Index (DESI) 2022: Human Capital. [online] Available at: https://digital-strategy.ec.europa.eu/en/policies/desi-human-capital [Accessed 27 May 2025].
Kritzinger, W., Karner, M., Traar, G., Henjes, J. & Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), pp.1016–1022. https://doi.org/10.1016/j.ifacol.2018.08.474
Lupton, D. (2015). Digital Sociology. London: Routledge.
Meta Platforms, Inc. (2025). Meta Privacy Policy Update. [online] Available at: https://www.meta.com/privacy/policy/[Accessed 27 May 2025].
Noble, S.U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. New York: NYU Press.
OECD.AI. (2022). OECD Artificial Intelligence Policy Observatory. [online] Available at: https://oecd.ai/ [Accessed 27 May 2025].
UNESCO Institute for Statistics. (2018). A Global Framework of Reference on Digital Literacy Skills for Indicator 4.4.2. [online] Available at: https://uis.unesco.org/sites/default/files/documents/ip51-global-framework-reference-digital-literacy-skills-2018-en.pdf [Accessed 27 May 2025].
United Nations. (2020). Roadmap for Digital Cooperation. [online] Available at: https://www.un.org/en/content/digital-cooperation-roadmap/ [Accessed 27 May 2025].
World Bank. (2016). World Development Report 2016: Digital Dividends. Washington, DC: World Bank.
World Economic Forum. (2023). Women in tech interview: Girls Who Code CEO Tarika Barrett. [online] Available at: https://www.weforum.org/stories/2023/10/more-women-hired-tech-sector-girls-who-code/ [Accessed 27 May 2025].