Facets of algorithmic literacy: Information, experience, and individual factors predict attitudes toward algorithmic systems

Abstract

Algorithmic decision-making systems are ubiquitous in digital media, but the public has been largely unable to negotiate the role of algorithms in society. Building from the concept of attitude-behavior consistency for political behavior, we develop a framework for fostering algorithmic literacy to develop well-informed attitudes toward algorithms. As algorithms are increasingly relevant to broad societal effects, an integrative approach is needed for a full account of how the public makes sense of algorithms and their role in society. We designed and tested a novel intervention that combines algorithmic literacy with personalized user experiences to see how each component influenced attitudes toward algorithms. We found these methods jointly informed attitudes, but the intervention’s efficacy was dependent on participants’ individual differences in technology use.

What do we know about algorithmic literacy? The status quo and a research agenda for a growing field

“…However, broader frameworks (e.g., Lomborg & Kapsch, 2020;Swart, 2021a) note the importance of affective and behavioral dimensions as part of holistic literacy. Knowledge about algorithms is not strongly correlated to positive attitudes (Araujo et al, 2020;Dietvorst et al, 2015;Yeomans et al, 2019), and increasing this knowledge does little to change these attitudes (Silva et al, 2022). This highlights that affective dimensions of literacy function separately from knowledge, and likely depend on users' needs and motivations for using any specific algorithmically driven platform.…”

“…One method in need of development are experimental studies of algorithmic literacy effects on various cognitive, affective, and behavioral outcomes. One such intervention has tested the effects of exposure to algorithmic information and found changes to users' attitudes about algorithms (Silva et al, 2022). Computational approaches are another avenue for more advanced assessments of algorithmic literacy, potentially through the collection and display of social media data to its users for reflection.…”

AI Art and Misinformation: Approaches and Strategies for Media Literacy and Fact Checking

“…Hence, the need for a data literacy within media literacy in order to understand the potential impacts of AI, given that the datasets themselves are frequently problematic [29]. Even more specifically, there have been calls for an algorithmic literacy [47].…”

Brushing Society Against the Grain: Digital Footprints, Scraps, Non-Human Acts, Crumbs, and Other Traces

“…Other authors point out the differences between data and algorithmic awareness versus literacy. Silva et al (2022, p. 4) state that algorithmic literacy is attained by developing “knowledge of the goals prioritized by algorithmic designers and their impact on society” and through examination of “the technical aspects of algorithmic systems.” For Dogruel et al (2021), literacy is composed by two dimensions: “the combination of being aware of the use of algorithms in online applications, platforms, and services and knowing how algorithms work” (Dogruel et al, 2021, p. 116). Similarly, Hargittai et al (2020, p. 771), underline the differences between algorithmic awareness and understanding, defining the latter as “having some sense of how systems process information about users, and how they can and may use information they have about the user when they present content to said user.” Moreover, by understanding how their behavior contributes to shape algorithmic systems, “algorithm literates” have “the potential to shape algorithmic operations” (Dogruel et al, 2021, p. 116).…”