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The news that young people consume is increasingly subject to algorithmic curation. Yet, while numerous studies explore how algorithms exert power in citizens’ everyday life, little is known about how young people themselves perceive, learn about, and deal with news personalization. Considering the interactions between algorithms and users from an user-centric perspective, this article explores how young people make sense of, feel about, and engage with algorithmic news curation on social media and when such everyday experiences contribute to their algorithmic literacy. Employing in-depth interviews in combination with the walk-through method and think-aloud protocols with a diverse group of 22 young people aged 16–26 years, it addresses three current methodological challenges to studying algorithmic literacy: first, the lack of an established baseline about how algorithms operate; second, the opacity of algorithms within everyday media use; and third, limitations in technological vocabularies that hinder young people in articulating their algorithmic encounters. It finds that users’ sense-making strategies of algorithms are context-specific, triggered by expectancy violations and explicit personalization cues. However, young people’s intuitive and experience-based insights into news personalization do not automatically enable young people to verbalize these, nor does having knowledge about algorithms necessarily stimulate users to intervene in algorithmic decisions.

PurposeAs algorithms permeate nearly every aspect of digital life, artificial intelligence (AI) systems exert a growing influence on human behavior in the digital milieu. Despite its popularity, little is known about the roles and effects of algorithmic literacy (AL) on user acceptance. The purpose of this study is to contextualize AL in the AI environment by empirically examining the role of AL in developing users' information processing in algorithms. The authors analyze how users engage with over-the-top (OTT) platforms, what awareness the user has of the algorithmic platform and how awareness of AL may impact their interaction with these systems.Design/methodology/approachThis study employed multiple-group equivalence methods to compare two group invariance and the hypotheses concerning differences in the effects of AL. The method examined how AL helps users to envisage, understand and work with algorithms, depending on their understanding of the control of the information flow embedded within them.FindingsOur findings clarify what functions AL plays in the adoption of OTT platforms and how users experience algorithms, particularly in contexts where AI is used in OTT algorithms to provide personalized recommendations. The results point to the heuristic functions of AL in connection with its ties in trust and ensuing attitude and behavior. Heuristic processes using AL strongly affect the credibility of recommendations and the way users understand the accuracy and personalization of results. The authors argue that critical assessment of AL must be understood not just about how it is used to evaluate the trust of service, but also regarding how it is performatively related in the modeling of algorithmic personalization.Research limitations/implicationsThe relation of AL and trust in an algorithm lends strategic direction in developing user-centered algorithms in OTT contexts. As the AI industry has faced decreasing credibility, the role of user trust will surely give insights on credibility and trust in algorithms. To better understand how to cultivate a sense of literacy regarding algorithm consumption, the AI industry could provide examples of what positive engagement with algorithm platforms looks like.Originality/valueUser cognitive processes of AL provide conceptual frameworks for algorithm services and a practical guideline for the design of OTT services. Framing the cognitive process of AL in reference to trust has made relevant contributions to the ongoing debate surrounding algorithms and literacy. While the topic of AL is widely recognized, empirical evidence on the effects of AL is relatively rare, particularly from the user's behavioral perspective. No formal theoretical model of algorithmic decision-making based on the dual processing model has been researched.

The exponential growth of algorithms has made establishing a trusted relationship between human and artificial intelligence increasingly important. Algorithm systems such as chatbots can play an important role in assessing a user’s credibility on algorithms. Unless users believe the chatbot’s information is credible, they are not likely to be willing to act on the recommendation. This study examines how literacy and user trust influence perceptions of chatbot information credibility. Results confirm that algorithmic literacy and users’ trust play a pivotal role in how users form perceptions of the credibility of chatbot messages and recommendations. Insights on how user trust is related to credibility provide a useful perspective on the conceptualization of algorithmic credibility. Algorithmic information processing that has been identified provides better foundations for algorithm design and development and a stronger basis for the design of sense-making chatbot journalism.

Social media users have long been aware of opaque content moderation systems and how they shape platform environments. On TikTok, creators increasingly utilize algospeak to circumvent unjust content restriction, meaning, they change or invent words to prevent TikTok’s content moderation algorithm from banning their video (e.g., “le$bean” for “lesbian”). We interviewed 19 TikTok creators about their motivations and practices of using algospeak in relation to their experience with TikTok’s content moderation. Participants largely anticipated how TikTok’s algorithm would read their videos, and used algospeak to evade unjustified content moderation while simultaneously ensuring target audiences can still find their videos. We identify non-contextuality, randomness, inaccuracy, and bias against marginalized communities as major issues regarding freedom of expression, equality of subjects, and support for communities of interest. Using algospeak, we argue for a need to improve contextually informed content moderation to valorize marginalized and tabooed audiovisual content on social media.

This paper discusses educating stakeholders of algorithmic systems (systems that apply Artificial Intelligence/Machine learning algorithms) in the areas of algorithmic fairness, accountability, transparency and ethics (FATE). We begin by establishing the need for such education and identifying the intended consumers of educational materials on the topic. We discuss the topics of greatest concern and in need of educational resources; we also survey the existing materials and past experiences in such education, noting the scarcity of suitable material on aspects of fairness in particular. We use an example of a college admission platform to illustrate our ideas. We conclude with recommendations for further work in the area and report on the first steps taken towards achieving this goal in the framework of an academic graduate seminar course, a graduate summer school, an embedded lecture in a software engineering course, and a workshop for high school teachers.

This article addresses the need for algorithmic literacy in the field of journalism and media education. Amid the escalating complexity of disinformation in the media landscape, the aim is to enhance users’ awareness and understanding of algorithm-driven content. Through focused research on communication students, the study investigates attitudes, beliefs and knowledge relating to the influence of algorithmic systems on news consumption. Existing scholarship is surveyed to establish the evolving nature of algorithmic literacy, ranging from optimizing search engines to countering misconceptions among digital natives. The relevance of digital information dissemination theories such as incidental consumption, news-finds-me perception, echo chambers and filter bubbles is highlighted in understanding algorithm-driven news selection. Methodologically, two focus groups of communication students from universities in Spain and the United States engage in discussions on critical consumption attitudes, algorithmic beliefs and knowledge. The outcomes reveal the students’ skepticism towards algorithmic news selection and their awareness of emotional triggers shaping news dissemination. Notably, they differentiate between valuable news and trends influenced by algorithms. Conclusions underscore the significance of the “WITH” (Why-Is-This-Here) perception as an indicator of critical consumption and the need for algorithmic literacy. The insights of communication students contribute to algorithmic systems, and their familiarity varies, yet they recognize the impact on news consumption. This study advocates for algorithmic literacy to empower citizens for responsible news consumption and journalism.

Purpose The purpose of this paper is to introduce and examine algorithmic culture and consider the implications of algorithms for information literacy practice. The questions for information literacy scholars and educators are how can one understand the impact of algorithms on agency and performativity, and how can one address and plan for it in their educational and instructional practices? Design/methodology/approach In this study, algorithmic culture and implications for information literacy are conceptualised from a sociocultural perspective. Findings To understand the multiplicity and entanglement of algorithmic culture in everyday lives requires information literacy practice that encourages deeper examination of the relationship among the epistemic views, practical usages and performative consequences of algorithmic culture. Without trying to conflate the role of the information sciences, this approach opens new avenues of research, teaching and more focused attention on information literacy as a sustainable practice. Originality/value The concept of algorithmic culture is introduced and explored in relation to information literacy and its literacies.

Algorithmic systems that provide services to people by supporting or replacing human decision-making promise greater convenience in various areas. The opacity of these applications, however, means that it is not clear how much they truly serve their users. A promising way to address the issue of possible undesired biases consists in giving users control by letting them configure a system and aligning its performance with users’ own preferences. However, as the present paper argues, this form of control over an algorithmic system demands an algorithmic literacy that also entails a certain way of making oneself knowable: users must interrogate their own dispositions and see how these can be formalized such that they can be translated into the algorithmic system. This may, however, extend already existing practices through which people are monitored and probed and means that exerting such control requires users to direct a computational mode of thinking at themselves.

Traditional information literacy skills (e.g., effectively finding and evaluating information) need to be updated due to the rapidly changing information ecosystem and the growing dominance of online platforms that use algorithms to control and shape information. This article proposes additions to the current ACRL Framework for Information Literacy for Higher Education that relate to algorithmic literacy. The \"Authority is Constructed and Contextual\" frame can be applied to recognizing the need to question algorithmic authority (including algorithmic bias), the Information Has Value\" frame can be used to acknowledge online platforms' use of proprietary algorithms allowing third parties to access personal data, and the \"Searching as Strategic Exploration\" frame can draw attention to search results in online platforms are mediated through algorithms. Classroom activities to teach the new knowledge practices and dispositions are also included.

With the deep development and iterative upgrading of algorithmic technology, the management practice of platform enterprises using intelligent algorithmic technology has become a hot issue. However, there is little research on the impact of perceived algorithmic control on work engagement from the perspective of platform workers. Drawing upon the regulatory focus theory, this study constructs a “double-edged sword” model to test the impact of perceived algorithmic control on platform workers’ work engagement by focusing on the positive mediating role of promotion-focused job crafting, the negative mediating role of prevention-focused job crafting, and the moderating role of algorithmic literacy. The data collected from 302 platform workers in China were used for an empirical study, and corresponding analyses were carried out to verify the theoretical model constructed by using SPSS and Mplus. The findings indicate the following: (a) perceived algorithmic control positively affects work engagement through promotion-focused job crafting; (b) perceived algorithmic control negatively affects work engagement indirectly through prevention-focused job crafting; (c) the indirect effect of perceived algorithmic control on work engagement via promotion-focused job crafting is stronger when there is a high level of algorithmic literacy and weaker in the case of low algorithmic literacy; and (d) the indirect effect of perceived algorithmic control on work engagement via prevention-focused job crafting is weaker in situations of high algorithmic literacy and stronger in those of low algorithmic literacy. The findings not only enrich theoretical studies on algorithmic control and work engagement but also offer guidance to platform-based enterprises on how to leverage the positive aspects of algorithmic control to better support individuals with different traits.