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This paper aims to explore explainable artificial intelligence (XAI) in democracy, proposing an applicable framework. With artificial intelligence’s (AI) increasing use in democracies, the demand for transparency and accountability in AI decision-making is recognized. XAI addresses AI “black boxes” by enhancing model transparency.

This study includes a thorough literature review of XAI. The methodology chosen was design science research to enable design theory and problem identification about XAI’s state of the art. Thereby finding and gathering crucial information to build a framework that aims to help solve issues and gaps where XAI can be of major influence in the service of democracy.

This framework has four main steps to be applied in the service of democracy by applying the different possible XAI techniques that may help mitigate existing challenges and risks for the democratic system. The proposed artifact intends to display and include all the necessary steps to select the most suitable XAI technology. Examples were given for every step of the artifact to provide a clear understanding of what was being proposed.

An evaluation of the proposed framework was made through interviews with specialists from different areas related to the topics in the study. The interviews were important for measuring the framework’s validity and originality.

This empirical study investigates the potential impact on freedom of expression arising from the accumulation of user-generated content on the web. The purpose of this study is to serve as a valuable reference for countries and regions that have not yet implemented web archiving due to similar concerns.

To achieve the goals, the author conducted a web-based survey experiment using sentiment analysis of book reviews as a representation of general topics. This approach enabled the author to objectively examine whether the expression of content undergoes changes in accordance with social conformity theory.

The study’s findings suggest that, at least for general topics, the observed chilling effect is minimal at best. This provides support for the proposition that it is advisable to proceed to the subsequent phase, where more sensitive subjects can be thoroughly explored in the context of web archiving and its associated chilling effects.

To the best of the author’s knowledge, this study is the first attempt to conduct a survey experiment addressing potential chilling effects resulting from the collection of user-generated content. Notably, the measurement of chilling effects remains contentious and comes with inherent limitations, adding a nuanced perspective to the discourse.

The purpose of this conceptual paper is to describe how the affinity space concept has been used to frame learning via social media, and call for and discuss a refresh of the affinity space concept to accommodate changes in social media platforms and algorithms.

Guided by a sociocultural perspective, this paper reviews and discusses some ways the affinity space concept has been used to frame studies across various contexts, its benefits and disadvantages and how it has already evolved. It then calls for and describes a refresh of the affinity space concept.

Although conceptualized 20 years ago, the affinity space concept remains relevant to understanding social media use for learning. However, a refresh is needed to accommodate how platforms have changed, algorithms’ evolving role in social media participation and how these technologies influence users’ interactions and experiences. This paper offers three perspectives to expand the affinity space concept’s usefulness in an increasingly platformized and algorithmically mediated world.

This paper underscores the importance of algorithmic literacy for learners and educators, as well as regulations and guidance for social media platforms.

This conceptual paper revisits and updates a widely utilized conceptual framing with consideration for how social media platform design and algorithms impact interactions and shape user experiences.

This article reports on findings from interviews with ICT-based content creators whose work focuses on documenting and curating queer history and culture. The research specifically examines how as amateur historians, the participant’s embodied knowledge plays a central role in how they engage with discourse about queer historical figures, methods of queer historiography and community accountability.

The research deploys a queer constructivist framework to qualitatively gather and analyzes the semi-structured interviews of 31 North American content creators who curate digital project related to queer history and culture. The interviews were gathered between August 2022 and August 2023.

The research highlights how the subjectivity of queer embodiment aids, rather than hinders, participants' ability to collaborate with LGBTQIA+ communities while also addressing more significant ethical questions around intersectionality and inclusive historiographic work.

The content creators’ own positionality and commitments to community accountability and queer inclusivity fostered richer stories and historical documentation, while also helping make visible queer identity as affirming and valuable within queer culture. Additionally, practical implications include highlighting the value of ICT-based content within the distribution of educational and informational resources related to queer history.

This research offers an underexamined intersection of historiography and queer embodiment. While extensive scholarship on institutional and community-based historiography work exist the content creators interviewed within this study exist within the space of both, often using a combination of embodied knowledge and traditional curatorial work to translate between such spaces, inviting, in turn, new ways of thinking about queer archival knowledge.

This study aims to explore children’s cognitions about data flows online and their understandings of algorithms, often referred to as algorithmic literacy or algorithmic folk theories, in their everyday uses of social media and YouTube. The authors focused on children ages 8 to 11, as these are the ages when most youth acquire their own device and use social media and YouTube, despite platform age requirements.

Nine focus groups with 34 socioeconomically, racially and ethnically diverse children (8–11 years) were conducted in California. Groups discussed data flows online, digital privacy, algorithms and personalization across platforms.

Children had several misconceptions about privacy risks, privacy policies, what kinds of data are collected about them online and how algorithms work. Older children had more complex and partially accurate theories about how algorithms determine the content they see online, compared to younger children. All children were using YouTube and/or social media despite age gates and children used few strategies to manage the flow of their personal information online.

The paper includes implications for digital and algorithmic literacy efforts, improving the design of privacy consent practices and user controls, and regulation for protecting children’s privacy online.

Research has yet to explore what socioeconomically, racially and ethnically diverse children understand about datafication and algorithms online, especially in middle childhood.

This paper investigates the digital information practices of Afro-Latino youth, focusing on their engagement with mental health content on TikTok. It aims to understand how racial and ethnic identity dimensions shape their information behaviors in digital spaces.

Employing qualitative methods, the study involved interviews with thirteen Afro-Latino teens. This exploratory approach, draws connections between Afro-Latino identity and information practices using three constructs: (i) typology of information practices, (ii) intersectionality, and (iii) assemblages.

The study reveals that Afro-Latino youth actively construct “information assemblages” and “algorithmic counterspaces” on TikTok, enabling them to engage with content that resonates with their identities. However, it also highlights the challenges posed by these spaces' temporary and algorithm-dependent nature in maintaining consistent engagement with mental health information.

The research is limited by its small sample size and focus on a single platform, which may affect generalizability. Future research should explore other platforms, and draw a deeper distinction between content creators and other users.

This paper underscores the need for designers and educators to prioritize the importance of algorithmic literacy and design affordances that empower users to transparently understand algorithmic functionality, so as to support on-going engagement with algorithmic counterspaces.

This research offers novel insights into the digital information practices of Afro-Latino youth, a typically underrepresented group in academic research. It introduces new concepts in information science and digital media studies, highlighting the importance of intersectional identities in digital information practices.

The notion of using a generative artificial intelligence (AI) engine for text composition has gained excessive popularity among students, educators and researchers, following the introduction of ChatGPT. However, this has added another dimension to the daunting task of verifying originality in academic writing. Consequently, the market for detecting artificially generated content has seen a mushroom growth of tools that claim to be more than 90% accurate in sensing artificially written content.

This research evaluates the capabilities of some highly mentioned AI detection tools to separate reality from their hyperbolic claims. For this purpose, eight AI engines have been tested on four different types of data, which cover the different ways of using ChatGPT. These types are Original, Paraphrased by AI, 100% AI generated and 100% AI generated with Contextual Information. The AI index recorded by these tools against the datasets was evaluated as an indicator of their performance.

The resulting figures of cumulative mean validate that these tools excel at identifying human generated content (1.71% AI content) and perform reasonably well in labelling AI generated content (76.85% AI content). However, they are perplexed by the scenarios where the content is either paraphrased by the AI (39.42% AI content) or generated by giving a precise context for the output (60.1% AI content).

This paper evaluates different services for the detection of AI-generated content to verify academic integrity in research work and higher education and provides new insights into their performance.

Existing literature has begun to identify the agonistic and contested aspects of the ongoing development of accountability systems. These “contests” are particularly important during periods of change when an accountability “deficit” has been identified, that is, when existing accountability systems are deemed inadequate and requiring revision. The purpose of this paper is to explore one such set of contests in the case of large technology and social media firms: the so-called “big tech”. The authors focus specifically on “big tech” because of increasing societal concerns about the harms associated with their products, services and business practices.

The authors analysed four US Congressional hearings, in which the CEO of Facebook was held to account for the company's alleged breaches and harms. The authors conducted a discourse analysis of the dialogue between the account giver (Mark Zuckerberg) and account holders (Members of Congress) in the oral testimony at the four hearings.

Two areas of contestation in the dialogue between the account giver and account holders are identified. “Epistemic contests” involved contestation about the “facts” concerning the harms the company had allegedly caused. “Responsibility contests” involved contestation about who (or what) should be held responsible for these harms and according to what standards or criteria.

The study advances critical dialogical accountability literature by identifying two areas of contestation during periods of change in accountability systems. In so doing, they advanced the theory by conceptualising the process of change as underpinned by discursive contests in which multiple actors construct and contest the “problem” with existing accountability systems. The outcomes of these contests are significant, the authors suggest, because they inform the development of reforms to the accountability system governing big tech firms and other industries undergoing similar periods of contestation and change.

The present study undertakes an extensive review of the causes of service failures in artificial intelligence (AI) technology literature.

A hybrid review has been employed which includes descriptive analysis, and bibliometric analysis with content analysis of the literature approach to synthesizing existing research on a certain topic. The study has followed the SPAR-4-SLR protocol as outlined by Paul et al. (2021). The search period encompasses the progression of service failure in AI from 2001 to 2023.

From identified theories, theoretical implications are derived, and thematic maps direct future research on topics such as data mining, smart factories, and among others. The key themes are being proposed incorporates technological elements, ethical deliberations, and cooperative endeavours.

This research study makes a valuable contribution to understanding and reducing service defects in AI by providing insights that can inform future investigations and practical implementations. Six key future research directions are derived from the thematic and cluster discussions presented in the content analysis.

This paper aims to consider the role of social media platforms as educational technologies given growing evidence of harms to democracy, society and individuals, particularly through logics of efficiency, racism, misogyny and surveillance inextricably designed into the architectural and algorithmic bones of social media. The paper aims to uncover downsides and drawbacks of for-profit social media, as well as consider the discriminatory design embedded within its blueprints.

The authors used a method of a technological audit, framed through the lenses of technoskepticism and discriminatory design, to consider the unintended downsides and consequences of Twitter and Instagram.

The authors provide evidence from a variety of sources to demonstrate that Instagram and Twitter’s intersection of technological design, systemic oppression, platform capitalism and algorithmic manipulation cause material harm to marginalized people and youth.

The authors contend that it is a conflict of professional ethics to treat social media as an educational technology that should be used by youth in educational settings. Thus, they suggest that future scholarship focus more on addressing methods of teaching about social media rather than teaching with social media.

The paper concludes with recommendations for educators who might work alongside young people to learn about social media while taking informed social actions for more just technological futures.

This paper fulfills an identified need to challenge the direction of the field of social media and education research. It is of use to education scholars, practitioners and policy makers.