In an age of ubiquitous data mining, analytics and AI, life chances are at stake through algorithmic sorting and management, ratings, scoring, and a host of data‐driven practices.
China’s social credit system is one well known example, but there are many others. A series of articles in the Guardian looks at how algorithms negatively affect the poor in Australia, India and the UK. Disadvantaged groups tend to fall behind also online, for example through limited access to technology, restricted opportunities for use, or a lack of key digital skills.
Three divides define research on the unequal effects of digital technologies, which has developed from a focus on access, to skills and uses of these, and most recently to real-world outcomes of their use.
Access – The First-Level Digital Divide
Most will be familiar with the first-level digital divide, between those with and without access to information technology (ICT). Considerable differences remain both between countries and within countries. Even within richer countries, some groups (for example the elderly, those living in rural areas or those with a low level of education) struggle to access the internet.
At the same time, new scholarship is moving the focus to different types of devices and functionalities. Smart-enabled devices (speakers, homes, phones etc.) provide alternative ways of accessing the internet. There are important differences between using your smart phone (not to mention a smart fridge) to get online, and using your laptop.
Complex but beneficial tasks such as editing text documents or spreadsheets are much more difficult or impossible on a mobile or other smart device. This has led to the notion of a mobile Internet underclass, characterized by extractive rather than productive forms of internet access.
Skills – The Second-Level Digital Divide
The second-level digital divide refers to inequalities in skills and uses of ICT. One example is online participation and use of social media, where research has distinguished between active content creators and passive consumers. Age and to some extent gender are important factors in this regard.
More recently, research on the second-level divide has focused on new phenomena at the intersection of leisure and business. The so-called sharing economy, platform economy or gig economy raises issues of surveillance and control, and collective action – as demonstrated recently by the strike of Foodora workers in Oslo.
An under-researched question in this respect is whether economically advantaged workers are better able to avoid algorithmic control and organize collectively. Digital inequalities scholarship could have an important voice in pointing to the ways in which social factors, including social milieu and different forms of capital influence algorithmic practices and literacies.
Outcomes – The Third-Level Digital Divide
The third-level digital divide looks at concrete outcomes of internet use, drawing attention to users’ ability to translate use of digital technologies into favorable offline outcomes. Outcomes are mostly measured through surveys asking, for example, whether respondents saved money by using the internet, found information that helped them improve their health, or have more contact with family and friends.
The ‘Matthew’ effect describes a dynamic whereby the rich get richer, and the poor get poorer. Digital inequalities scholars provide rich evidence for such Matthew effects when it comes to emerging technologies. For example, famed venture capitalists the Winklevoss brothers invested heavily in cryptocurrencies and became the first crypto-billionaires in 2018. Meanwhile significant barriers remain before bitcoin can be used to promote financial inclusion and combat poverty.
While research on the third-level digital divide so far has been focused on how new technologies lead to unequal benefits for different groups, future scholarship should also look at the harmful consequences of what people do online and what data traces are associated with them.
Underprivileged internet users are more likely to fall victim to fraudulent offers or predatory websites. In the United States, poor women have been shown to be a test subject for surveillance technology, because they are heavily monitored by the government. In an age of big data and AI, where most interactions that happen through and with digital technologies are tracked, digital inequalities are a more important topic to address than ever.
Reference:
Lutz, C. Digital inequalities in the age of artificial intelligence and big data. Hum Behav & Emerg Tech. 2019; 1: 141– 148.
Text:
Knut Myrum Næss , Communication Advisor, BI