Algorithmic literacy in the smart city: A new digital divide?


Anabel Quan-Haase, University of Western Ontario

Artificial intelligence or AI is not new. Developments in AI can be traced back to the 1950s, when scientists such as Alan Turing proposed models of machine learning that emulated human thinking. Yet, AI has seen new breakthroughs in the past decade with the widespread adoption of multimodal large language models such as GPT-1 to –4. While much of the study of AI has focused on its development and adoption, less is known about its social implications. According to Floridi et al. (2018), the current debate on AI’s impact on society requires a focus on how far the impact will be positive or negative and the “pertinent questions now are by whom, how, where, and when this positive or negative impact will be felt” (p. 690). Initial sociological work has started to look at AI literacy, with new scales and developments aiming to uncover inequalities in the use of AI and people’s understanding of its social implications. Despite these recent developments, no study today has looked at urban AI. Batty (2018) outlines how the large-scale implementation of AI and its filtering into urban spaces and infrastructures generates a new urbanism, referred to as AI urbanism. Luusua et al. (2023) define ‘Urban AI’ as the study of the relationship between “artificial intelligence systems and urban contexts, including the built environment, infrastructure, places, people, and their practices” (p.1039). In order to understand how people participate in different types of urban AI, and whether this is experienced differently with respect to digital divides we propose to investigate their level of algorithmic literacy of urban AI. Studying how people participate with algorithms is valuable for understanding how users navigate and evaluate algorithmically curated spaces, and this has been described as ‘algorithm literacy’ (Dogruel, 2021; Shin, Rasul and Fotiadis, 2022; Silva, Chen and Zhu, 2022) or ‘algorithm awareness’ (Gran, Booth and Bucher, 2021). Dogruel defines ‘algorithm literacy’ as the combination of “being aware of the use of algorithms in online applications, platforms, and services and knowing how algorithms work (i.e., understanding the types, functions, and scopes of algorithmic systems on the internet” (Dogruel 2022, p. 116). Recent studies on algorithmic literacy with respect to digital inequalities have found that it is often less visible than the previously recognised digital divides, such as digital access and digital skills, and that algorithmic systems impact peoples’ lives in different and often unequal, ways (Cotter and Reisdorf, 2020; Dogruel, 2021; Dogruel, Masur and Joeckel, 2022; Gran, Booth and Bucher, 2021). Gran et al. establish, in their study of algorithmic literacy in Norway, a typology of algorithm awareness that ranges from a) The unaware; b) The uncertain; c) The affirmative; d) The neutral; e) The sceptic and f) The critical, and found that over 40% of the study participants lacked an awareness of AI (Gran, Booth and Bucher, 2021), which demonstrates how participation in AI is an important aspect for digital inequalities. Therefore studying algorithmic literacy of urban AI with respect to different demographic characteristics may help to give insight into whether it can exacerbate digital divides in the city. We will outline an early-stage study with inhabitants in London, Ontario on their awareness and understanding of urban AI using an algorithmic literacy scale. We will discuss the outcomes of the study in terms of how digital divides can shape the way AI is experienced in the city. On a broader level we will discuss how there is a need to address the socio-technological barriers with urban AI and the implications for smart city projects.

Non-presenting author: Katherine Willis, University of Plymouth

This paper will be presented at the following session: