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This paper examines the work practices involved in making data legible to machines and machine output legible to humans. The study is based on ethnographic research of a team of art experts at DNArt – a data classification system that features a growing database of art images, a classification scheme, a similarity matching algorithm, and a website that together serve as a consumer judgment device in an emerging online market for art. I analyze interactions from meeting observations, interviews, documentation, and online interaction data to show how non-technical art experts explain and repair sociotechnical breakdowns – when their expectations for similarity between art images and artists differ from the similarity relations produced by the algorithm. By repairing breakdowns, the art experts construct the algorithm anew, as a legitimate revealer of similarity in art. In doing so, the team's repair work is folded back into the black box of the algorithm, rendering it invisible and unacknowledged, sometimes even by the experts themselves. [ABSTRACT FROM AUTHOR]

The paper aims at creating a bridge between media and migration studies and critical algorithm studies. By adopting a media ecological approach and a mutual shaping of technology and society perspective, in this paper, we explore the factors that lead, especially in Italy, to discriminant and stigmatizing image search results, related to specific groups of immigrants living in the country. We performed a content analysis of Google-Images search results with regard to the largest immigrant communities hosted in France, Germany, Italy, and the United Kingdom. Results show that the depiction of Romanian, Albanian, Moroccan, and Algerian immigrant communities on Google.it is flattened on a univocal stigmatized representation that shows them as criminals, which is not the case in other countries. Most of these stigmatizing images derive from local online newspapers, which questions the interplay between newsmaking choices and routines, and algorithms logics. [ABSTRACT FROM AUTHOR]

In recent years, deepfakes have become part and parcel of contemporary algorithmic culture. It is regularly claimed that they have the potential to introduce novel modes of societal disruption, violence, and harm. Yet, over-emphasising the power of deepfakes risks occluding frictions, struggles, and logics that already persist in the digital landscape. Arguing for a conceptualisation of deepfakes as an assemblage of differential tensions in society, we explore how they represent both a rupture and a continuation of the variegated politics of the image in the social world. The paper analyses the tensions of deepfakes through three distinct case studies: bodies, politics, and ideas of objectivity. Ultimately, we argue that the tensions and ethicopolitical implications of deepfakes are not reducible to a problem that can be solved through a logic of algorithmic detection and verification. [ABSTRACT FROM AUTHOR]

This article examines the datafication of ageing by drawing on a practice approach toward care. We describe the datafication of ageing as a matter of care, achieved through the local tinkering of actors – technology designers, care staff, older adults, and highlighting the practices necessary to develop, maintain and implement data infrastructures. This paper draws on research conducted in a qualitative interview study in a LTC facility that uses AI-supported sensors to detect, predict and alarm care staff about falls of older residents. 18 interviews with developers, staff, residents and interest groups were conducted, as well as 24 h of participant observation in the care facility. The results reveal how AI-development for older target groups is characterized by absent data on these populations. Designers turn to practices that decontextualize data from the realities of older adults, relying on domain experts or synthetic data. This decontextualization of data requires recontextualization, with staff and older residents ensuring that the system functions smoothly, adapting their behavior, protecting the system from making false decisions and making existing care arrangements 'fit' the databases used to monitor activities in these arrangements. The ambivalent position of older adults in this data assemblage is further highlighted, as their caring practices are made invisible by different actors through ageist stereotypes, positioning them as being too frail to understand and engage with the system. While their bodily behavior is core for the databases, their perspective on and engagements with the operating system are marginalized, rendering some aspects of ageing hyper-visible, and others invisible. [ABSTRACT FROM AUTHOR]

This paper argues that the social power of algorithms comes to the fore through the narratives they generate about individuals. Proposing the notion of 'algorithmic emplotment', the article showcases the ways in which algorithms construct and tell narratives about us, participating in shaping people's encounters with the world and their perceptions of it. The concept denotes the processes through which data, people, experiences, and complex temporalities are ordered, woven together, and presented as coherent, frictionless narratives in the present. Through an analysis of the smartphone feature called Apple Memories, the paper seeks to highlight the narratives algorithms tell, how they are constructed, and the potential impacts they may have on everyday life. The concept of algorithmic emplotment is used to scrutinise the ways in which people's lives are rendered sequential, ordered, and ultimately meaningful and actionable by algorithmic processes. [ABSTRACT FROM AUTHOR]

Digital Platforms consist of algorithms and rules that shape consumer behaviour. When faced with these embodiments of the platform's interests, how do consumers protect their own interests? Through multi-method, qualitative fieldwork focused on commuters using ride-hailing platforms in Metro Manila, this paper shows that consumers develop strategies to achieve better terms for themselves. This paper contributes to the literature on algorithmic control and user agency in two ways. First, it proposes a fine-grained typology of consumer strategies used in algorithmic digital platforms, consisting of 5 major types and 18 sub-types. Second, the typology sheds light on the distinct characteristics of consumer strategies and their implications. Future studies into user strategies, algorithmic systems, and digital platforms will benefit from the typology and implications laid out here. [ABSTRACT FROM AUTHOR]

The recent dispersion of algorithms throughout a large part of social life makes them valid analytical objects for sociology in the twenty-first century. The ubiquity of algorithms has led to increased public attention, scrutiny and, consequently, regulation. That is the focus of this paper. I will show that such regulatory processes are not just aimed at preventing certain algorithmic activities, but that they are also co-producing algorithms. They determine, in specific settings, what an algorithm is and what it ought to do. I will illustrate this by comparing two different European regulations aimed at algorithmic practices: the regulation of trading algorithms in the German High Frequency Trading Act and in the Markets in Financial Instruments Directive (MiFID II), and the regulation of personal data processing in the General Data Protection Regulation (GDPR). [ABSTRACT FROM AUTHOR]

Algorithms make highly consequential decisions and, thereby, exercise considerable power. In this study, I investigate how social power through algorithms is exercised in media consumption, particularly through curation algorithms. This conceptual paper then contributes to the understanding of social power through algorithms by suggesting the concept of algorithmic meta-capital. The concept derives from Bourdieu's theory on meta-capital which has also been applied to legacy media. I then argue that this algorithmic meta-capital is an extension of the power traditionally held by the state and legacy media. The study also contributes to the understanding of meta-capital as it proposes how the meta-capital possessed by digital intermediaries functions. It does so by legitimating representations of the world and by creating a necessity for algorithmic visibility across different fields, thereby shaping habitus. This Bourdieusian approach enables researchers to take a balanced view on the power of algorithms on the structure/agency continuum. [ABSTRACT FROM AUTHOR]

In the algorithmic era, both users and the platform battle for visibility. Chinese fans are savvy users who explore the hidden algorithms behind platform functions. With the collectively developed algorithmic imaginary, digital fandom communities negotiate with the platform over algorithms to optimize the visibility of celebrities they endorse. Drawing from participatory observation and semi-structured interviews in Chinese online fandoms of an idol group, INTO1, we detailed how fans as digital users collectively explore, interpret, and creatively utilize algorithms to increase their idol's visibility. We conclude that visibility, as a representation of algorithm power, is co-defined through the constant push-and-pull between digital users and the platform. This paper contributes to both algorithm and fandom studies by describing large-scale non-professional users' daily construction of the algorithmic imaginary in the unique context of Chinese fandom and beyond. It also discusses broader civic implications of fans' algorithmic practices to wider digital users in China. [ABSTRACT FROM AUTHOR]

This paper makes the case for considering the role of cross-cultural encounters in shaping developers' notions of information privacy. Recent studies on privacy by design shed light on developer practices yet tend to regard these workers as a generic category. The paper draws on two interviews with workers in a late-stage Israeli startup as a step toward localizing developers and the global products they design. The analysis identifies four narratives that juxtapose the local, the global and the commodification of users' personal information: (1) the origin myth of the company; (2) workers' personal and professional biographies; (3) reports on external regulations; and (4) accounts of work practices, rituals and communication formats. The analysis suggests how the globalization of the startup was implicated in changing ideas and practices relating to users' information. The paper concludes by discussing some of the challenges facing a culturally-sensitive study of developers as mediators of privacy. [ABSTRACT FROM AUTHOR]

Considerable scholarship has established that algorithms are an increasingly important part of what information people encounter in everyday life. Much less work has focused on studying users' experiences with, understandings of, and attitudes about how algorithms may influence what they see and do. The dearth of research on this topic globally with diverse populations may be in part due to the difficulty of studying a subject about which there is no known ground truth given that details about algorithms are proprietary and rarely made public. This paper explicitly takes on the methodological challenges of studying people's algorithm skills to shed light on the special considerations required when studying a topic about which even the researchers possess limited know-how. The paper advocates for more such scholarship to accompany existing system-level analyses of algorithms' social implications and offers a blueprint for how to do so. [ABSTRACT FROM AUTHOR]

Nowadays, the web offers a world of choice, providing millions of options at the click of a button. Web-based platforms such as Amazon.com and Yelp.com provide users with a range of tools and functionalities to navigate and compare options within a complex and bewildering landscape of choice. Whilst such websites afford users the feeling of objective and 'free' choice, it is also clear that the design features and architecture of websites have strategic affordances for structuring choice in different ways and for particular aims and objectives. Based on a study of 500 top-ranking websites, in this paper, we argue for a re-evaluation of the relationship between choice and the web, through an examination of the affordances of websites for shaping and governing choice. Using illustrative examples and drawing on a conceptual framework from previous work, we examine four interconnected aspects of how choice is structured through the affordances of websites. We discuss the role of algorithms in governing choice through websites, including their role in drawing users into a political economy of choice-making that appears to 'nudge' them in particular directions. Websites not only constitute and structure choice, but also presuppose, deploy, and potentially (re)produce the characteristics and capacities of 'choosers', and as choosers are not equals this in turn may reproduce and reinforce social inequalities and fissures. The paper concludes with a reflection on the entangled trajectories of choice and web technologies, and how this might impact our understanding of choice in contemporary society. [ABSTRACT FROM AUTHOR]

Algorithms are an integral part of our everyday lives and shape the selection and presentation of information and communication on the internet. At the same time, media users are faced with a lack of control and transparency when interacting with these systems because algorithms largely remain black boxes to end users. Relying on the notion that algorithms are socio-technical systems that comprise both technical and human components, this paper examines internet users' awareness of algorithms in different areas of internet use and inquires into users' perceptions of the impact of algorithms on their autonomy when interacting online. Empirically, we rely on qualitative interviews with 30 German internet users. Findings indicate that users in general are aware of algorithms operating in a wide range of applications and demonstrate a basic understanding of how these systems work. In line with the third-person effect, users perceive algorithms to have a stronger impact on others' internet use than on their own. Further, users' awareness of algorithms was found to be closely related to their perceived autonomy. When users feel in control of their interactions online, they are less aware of the impact of algorithms governing their interactions. Based on these results, we discuss the implications for transparency measures in algorithm regulation. [ABSTRACT FROM AUTHOR]

The introduction to this special issue suggests we need to develop ‘a greater understanding of what might be thought of as the social power of algorithms'. In this paper, ‘social power’ will be critically scrutinised through a study of the entanglement of algorithmic rules with contemporary video-based surveillance technologies. The paper will begin with an analysis of algorithmic ‘IF … THEN’ rules and the conditions (IF) and consequences (THEN) that need to be accomplished for an algorithm to be said to succeed. The work of achieving conditions and consequences demonstrates that the form of ‘power’ in focus is not solely attributable to the algorithm as such, but operates through distributed agency and can be noted as a network effect. That is, the conditions and consequences of algorithmic rules only come into being through the careful plaiting of relatively unstable associations of people, things, processes, documents and resources. From this we can say that power is not primarily social in the sense that algorithms alone create an impact on society, but social in the sense of power being derived through algorithmic associations. The paper argues that this kind of power is most clearly visible in moments of breakdown, failure or other forms of trouble, whereby algorithmic conditions and consequences are not met and the careful plaiting of associations has to be brought to the fore and examined. It is through such examinations that the associational dependencies more than the social power of algorithms are made apparent. [ABSTRACT FROM PUBLISHER]

The rapid development and dissemination of data analysis techniques permits the creation of ever more intricate algorithmic models. Such models are simultaneously the vehicle and outcome of quantification practices and embody a worldview with associated norms and values. A set of specialist skills is required to create, use, or interpret algorithmic models. The mechanics of an algorithmic model may be hard to comprehend for experts and can be virtually incomprehensible to non-experts. This is of consequence because such black boxing can introduce power asymmetries and may obscure bias. This paper explores the practices through which experts and non-experts determine the credibility of algorithmic models. It concludes that (1) transparency to (non-)experts is at best problematic and at worst unattainable; (2) authoritative models may come to dictate what types of policies are considered feasible; (3) several of the advantages attributed to the use of quantifications do not hold in policy making contexts. [ABSTRACT FROM AUTHOR]

Music streaming services all provide affordable and easy access to massive databases of music and instead attempt to increase customer loyalty by optimizing personalized recommendations and offering opportunities for listeners to build their 'own' music libraries. In this paper, these features are operationalized as the convenience, price, archive and algorithmic value of streaming services. Existing studies detail the multitude of ways of making sense of these services, but do not allow for specifying what features matter more. Drawing on an online survey and interviews, this study indicates that price, convenience and archive value predict continued intention to use music streaming services. Survey results negate the importance of algorithmic value, but the interviews suggest that for some, algorithmic individuation is too evasive to be noticed. Those who 'see' and value recommendations 'cater for algorithms' and consider their own listening and archiving practices as input for optimizing output. Combined, the different features set the stage for both overt and covert tethering of people and streaming services. [ABSTRACT FROM AUTHOR]

This paper explores the relationships of subjects in the context of data and data technologies, and advances an original theoretical framework called Informatic Personhood to better conceptual subjects and their relationships. Because of the enormous structural change that data has contributed to, subjects are sometimes distant and backgrounded in studies of data, despite data having significant impacts on their lives. Data-mediated relationships mean an increased scale to a relationship, with individuals able to connect to much broader contexts of data, but also have these structures reach down to their subjective context through data. Informatic Personhood seeks to capture the dynamics of data present in everyday life, addressing this distance and better conceptualising the scale of data-mediated relationships. This framework has two parts. The first – The Informatic Context – explores salient structural developments around data and conceptualises this as being defined by the presence of 'data interfaces' (that connect individuals to digital contexts), 'data circulation' (trends in the movement and storage of data), and 'data abstraction' (data manipulation practices). The second part concerns the Informatic Person, and the embodied, affective, and sensemaking relationships of individuals occurring across and through the Informatic Context. This framework better addresses the scale of data-mediated relationships, and places subjects firmly in the foreground of how data is understood. [ABSTRACT FROM AUTHOR]

In this paper, we use trolling to illuminate the entangled and complex relationship between online debate, technological infrastructure, and justice. While a great deal of research has investigated the harmful effects of trolling in the form of cyberbullying and online harassment, attention to the infrastructure of trolling can provide new insights into information flows within digital infrastructure, and consequently, bears on questions such as access to, and quality of, information; the status and credibility of knowledge claims and claimants; and about the gatekeepers of knowledge and information. We show how trolling takes advantage of the rapidity of information transmission and reproduction; technical illiteracy; automation; and 'soft' infrastructure such as conventions, protocols, etiquette, and rules governing online communities to affect informational justice using a methodologically symmetrical approach. [ABSTRACT FROM AUTHOR]

This paper draws on regulatory governance scholarship to argue that the analytic phenomenon currently known as ‘Big Data’ can be understood as a mode of ‘design-based’ regulation. Although Big Data decision-making technologies can take the form of automated decision-making systems, this paper focuses on algorithmic decision-guidance techniques. By highlighting correlations between data items that would not otherwise be observable, these techniques are being used to shape the informational choice context in which individual decision-making occurs, with the aim of channelling attention and decision-making in directions preferred by the ‘choice architect’. By relying upon the use of ‘nudge’ – a particular form of choice architecture that alters people’s behaviour in a predictable way without forbidding any options or significantly changing their economic incentives, these techniques constitute a ‘soft’ form of design-based control. But, unlike the static Nudges popularised by Thaler and Sunstein [(2008).Nudge. London: Penguin Books] such as placing the salad in front of the lasagne to encourage healthy eating, Big Data analytic nudges are extremely powerful and potent due to their networked, continuously updated, dynamic and pervasive nature (hence ‘hypernudge’). I adopt a liberal, rights-based critique of these techniques, contrasting liberal theoretical accounts with selective insights from science and technology studies (STS) and surveillance studies on the other. I argue that concerns about the legitimacy of these techniques are not satisfactorily resolved through reliance on individual notice and consent, touching upon the troubling implications for democracy and human flourishing if Big Data analytic techniques driven by commercial self-interest continue their onward march unchecked by effective and legitimate constraints. [ABSTRACT FROM PUBLISHER]

This paper outlines the notion of ‘algorithmic technique’ as a middle ground between concrete, implemented algorithms and the broader study and theorization of software. Algorithmic techniques specify principles and methods for doing things in the medium of software and they thus constitute units of knowledge and expertise in the domain of software making. I suggest that algorithmic techniques are a suitable object of study for the humanities and social science since they capture the central technical principles behind actual software, but can generally be described in accessible language. To make my case, I focus on the field of information ordering and, first, discuss the wider historical trajectory of formal or ‘mechanical’ reasoning applied to matters of commerce and government before, second, moving to the investigation of a particular algorithmic technique, theBayes classifier. This technique is explicated through a reading of the original work of M. E. Maron in the early 1960 and presented as a means to subject empirical, ‘datafied’ reality to aninterested readingthat confers meaning to each variable in relation to an operational goal. After a discussion of the Bayes classifier in relation to the question of power, the paper concludes by coming back to its initial motive and argues for increased attention to algorithmic techniques in the study of software. [ABSTRACT FROM PUBLISHER]

More and more aspects of our everyday lives are being mediated, augmented, produced and regulated by software-enabled technologies. Software is fundamentally composed of algorithms: sets of defined steps structured to process instructions/data to produce an output. This paper synthesises and extends emerging critical thinking about algorithms and considers how best to research them in practice. Four main arguments are developed. First, there is a pressing need to focus critical and empirical attention on algorithms and the work that they do given their increasing importance in shaping social and economic life. Second, algorithms can be conceived in a number of ways – technically, computationally, mathematically, politically, culturally, economically, contextually, materially, philosophically, ethically – but are best understood as being contingent, ontogenetic and performative in nature, and embedded in wider socio-technical assemblages. Third, there are three main challenges that hinder research about algorithms (gaining access to their formulation; they are heterogeneous and embedded in wider systems; their work unfolds contextually and contingently), which require practical and epistemological attention. Fourth, the constitution and work of algorithms can be empirically studied in a number of ways, each of which has strengths and weaknesses that need to be systematically evaluated. Six methodological approaches designed to produce insights into the nature and work of algorithms are critically appraised. It is contended that these methods are best used in combination in order to help overcome epistemological and practical challenges. [ABSTRACT FROM PUBLISHER]

Problems of bias and fairness are central to data justice, as they speak directly to the threat that 'big data' and algorithmic decision-making may worsen already existing injustices. In the United States, grappling with these problems has found clearest expression through liberal discourses of rights, due process, and antidiscrimination. Work in this area, however, has tended to overlook certain established limits of antidiscrimination discourses for bringing about the change demanded by social justice. In this paper, I engage three of these limits: 1) an overemphasis on discrete 'bad actors', 2) single-axis thinking that centers disadvantage, and 3) an inordinate focus on a limited set of goods. I show that, in mirroring some of antidiscrimination discourse's most problematic tendencies, efforts to achieve fairness and combat algorithmic discrimination fail to address the very hierarchical logic that produces advantaged and disadvantaged subjects in the first place. Finally, I conclude by sketching three paths for future work to better account for the structural conditions against which we come to understand problems of data and unjust discrimination in the first place. [ABSTRACT FROM AUTHOR]

The role of algorithms in decision-making is now so consequential that we agree that algorithmic systems must be designed, implemented, and managed in ways that are understandable to interested parties. Anchored in a tradition of capturing, contextualizing, and preserving evidence of analog and digital technologies, this article draws out the roles adopted or tried on by the recordkeeping professions to account for algorithmic systems as they communicate and represent meaning in the world. We begin by delineating a promising role for recordkeeping professions as algorithmic conceptualizers. We show how expertise in evaluating recorded information provides a method to conceive or model algorithms from an accountability and transparency perspective. Then we delineate a breakthrough role for recordkeeping professionals as algorithmic historicizers, revealing how the professions' curation of records of human actions extends to the capture and preservation of algorithms for purposes of critical review. Finally, we delineate an emerging role for recordkeeping professionals as algorithmic sponsors involved in harnessing algorithmic tools to manage and make accessible other forms of digital evidence at scale. In articulating such roles, our goal is to examine the opportunities inherent in harnessing the relationships that form at the intersection of algorithmic activity and evidentiary work. [ABSTRACT FROM AUTHOR]

This article proposes the concept ‘platformed racism’ as a new form of racism derived from the culture of social media platforms ‒ their design, technical affordances, business models and policies ‒ and the specific cultures of use associated with them. Platformed racism has dual meanings: first, it evokes platforms as amplifiers and manufacturers of racist discourse and second, it describes the modes of platform governance that reproduce (but that can also address) social inequalities. The national and medium specificity of platformed racism requires nuanced investigation. As a first step, I examined platformed racism through a particular national race-based controversy, the booing of the Australian Football League Indigenous star Adam Goodes, as it was mediated by Twitter, Facebook and YouTube. Second, by using an issue mapping approach to social media analysis, I followed the different actors, themes and objects involved in this controversy to account for the medium specificity of platforms. Platformed racism unfolded in the Adam Goodes controversy as the entanglement between users’ practices to disguise and amplify racist humour and abuse, and the contribution of platforms’ features and algorithms in the circulation of overt and covert hate speech. In addition, the distributed nature of platforms’ editorial practices ‒ which involve their technical infrastructure, policies, moderators and users’ curation of content ‒ obscured the scope and type of this abuse. The paper shows that the concept of platformed racism challenges the discourse of neutrality that characterises social media platforms’ self-representations, and opens new theoretical terrain to engage with their material politics. [ABSTRACT FROM PUBLISHER]

Our everyday practices are increasingly mediated through online technologies, entailing the navigation and also oft-simultaneous creation of large quantities of information and communication data. The scale and types of activities being undertaken, the data that are being created and engaged with, and the possibilities for analysis, archiving and distribution are now so extensive that technical constructs are necessarily required as a way to manage, interpret and distribute these. These constructs include the platforms, the software, the codes and the algorithms. This paper explores the place of the algorithm in shaping and engaging with the contemporary everyday. It does this via an exploration of some particular instances of algorithmic sorting and presentation as well as considering some of the ways these contribute to shaping our everyday practices and understandings. In doing so, it raises questions about understandings of agency and power, shifting world views and our complex relationship with technologies. [ABSTRACT FROM PUBLISHER]

Following concerns about social media's role in politics (fostering polarization and spreading disinformation), many activists and civic hackers have developed alternative digital democracy platforms for both deliberation and the representation of public opinion. But how are we to study the role of these platforms, and in particular, their algorithms in the development of issues and the publics that gather around them? This article employs a simple quali-quantitative data visualization to study how a particular digital democracy platform, vTaiwan (an implementation of Pol.is – a tool for generating opinions and consensus about public issues) – formats political participation. We investigate how one particular issue (Uber legalization) was formed and reformed by users, moderators, and algorithms on the vTaiwan platform over time. while the algorithm sorted opinions into a binary of pro and anti-Uber positions, we find that the comments themselves and their sequence suggest more nuanced positions and the potential for dialogue. We argue that vTaiwan may be limited by its focus on simple quantitative data points (positive or negative votes as opposed to the texts themselves) and a forced separation of participants into in-or-out opinion groups. This study contributes to critical algorithm studies and digital democracy studies by offering an effective way to analyse the role of algorithms in democratic politics. [ABSTRACT FROM AUTHOR]

This article discusses how Brazilian ubertubers – Uber drivers that manage YouTube channels focused on their riding experience – systematise and make public different ways of knowing surge pricing (SP), an algorithmic-oriented system that uses price adjustments to redistribute drivers across urban space. Taken by the Uber as an instrument to measure and regulate market conditions, SP mediates drivers' pragmatic and affective daily practices, materialising a asymmetrical power relation embedded into a neoliberal governmentality. The study explores 25 videos produced and shared by five consolidated Brazilian ubertubers, focusing on how this specific kind of digital influencer systematises and perform collective knowledge on how to increase earnings with surge pricing. The metaphors, hypotheses, and theories, the ubertuber's tactics to deal with SP's instability and with the risks of working on peripheral areas, and the efforts to investigate the logics of a 'new surge' are the main issues approached in the case study. In the conclusions, we discuss how ubertubers' ambivalent relations with surge pricing reveal their wider attempts to navigate neoliberal governmentality and precarious conditions. [ABSTRACT FROM AUTHOR]

This article advances extant research that has audited search algorithms for misinformation in four respects. Firstly, this is the first misinformation audit not to implement a national but a cross-national research design. Secondly, it retrieves results not in response to the most popular query terms. Instead, it theorizes two semantic dimensions of search terms and illustrates how they impact the number of misinformative results returned. Furthermore, the analysis not only captures the mere presence of misinformative content but in addition whether the source websites are affiliated with a key misinformation actor (Russia's ruling elites) and whom the conspiracy narratives cast as the malicious plotters. Empirically, the audit compares Covid-19 conspiracy theories in Google search results across 5 key target countries of Russia's foreign communication (Belarus, Estonia, Germany, Ukraine, and the US) and Russia as of November 2020 (N = 5280 search results). It finds that, across all countries, primarily content published by mass media organizations rendered conspiracy theories visible in search results. Conspiratorial content published on websites affiliated with Russia's ruling elites was retrieved in the Belarusian, German and Russian contexts. Across all countries, the majority of conspiracy narratives suspected plotters from China. Malicious actors from the US were insinuated exclusively by sources affiliated with Russia's elites. Overall, conspiracy narratives did not primarily deepen divides within but between national communities, since – across all countries – only plotters from beyond the national borders were blamed. To conclude, the article discusses methodological advice and promising paths of research for future cross-national search engine audits. [ABSTRACT FROM AUTHOR]

Filters and apps purporting to analyze the face and provide insight into a person's personality, heritage, or future have become a popular part of social media interactivity. This article examines the marketing and design of such products in relation to historical systems of racial, ethnic, moral, and psychological differentiation based on the face, such as physiognomy and eugenics. In particular, it explores user-generated 'Which Are You?' filters and apps such as FaceApp, Fantastic Face, and Gradient, which provide visual forecasting, horoscopes, and beauty and ethnic analysis based primarily on face scans. It argues that affinities of these automated optical tools to racist and discriminatory historical systems of face analysis – especially within the context of the representation or performance of identity on social media – can have important implications for the adoption of more extensive systems of facial detection and recognition. Packaging diagnostic and predictive face analysis in these ways can generate acceptance of, and support for, government and corporate applications of these technologies to identify or categorize individuals and predict their behavior, despite widespread social justice concerns over the design, accuracy, administration, and ethics of such systems. [ABSTRACT FROM AUTHOR]

Marking a decade of exciting interdisciplinary internet research, this is the 10thInformation, Communication and Societyspecial issue that features research generated by the annual Association of Internet Research (AoIR) conferences. This issue consists of eight provocative articles selected from #AoIR2016, the 17th annual conference, held at the Humboldt-Universität zu Berlin, Germany from 5–8 October 2016. The #AoIR2016 conference theme Internet Rules! invited participants to address the complex interplay of digital technologies, business models and user practices. For some, the Internet rules! Others are ruled by the internet. Reflecting the emergent focus during the conference, this special issue addresses the Internet as a set of connected platforms that have various technical, social, cultural, political and figurative meanings, and seeks to understand rules as a set of normative values. Offering a primer on platform values, the contributions share a commitment to social justice, offer innovative theoretical interventions and empirically ground the workings of platform values from various scholarly perspectives. They show how normative digitally networked technologies are mutually shaped by top-down decisions such as the profit-oriented workings of algorithms that differentially value some users over others and bottom-up user practices that both sustain and subvert value-laden mechanisms. [ABSTRACT FROM PUBLISHER]

Algorithmic profiling has become increasingly prevalent in many social fields and practices, including finance, marketing, law, cultural consumption and production, and social engagement. Although researchers have begun to investigate algorithmic profiling from various perspectives, socio-technical studies of algorithmic profiling that consider users' everyday perceptions are still scarce. In this article, we expand upon existing user-centered research and focus on people's awareness and imaginaries of algorithmic profiling, specifically in the context of social media and targeted advertising. We conducted an online survey geared toward understanding how Facebook users react to and make sense of algorithmic profiling when it is made visible. The methodology relied on qualitative accounts as well as quantitative data from 292 Facebook users in the United States and their reactions to their algorithmically inferred 'Your Interests' and 'Your Categories' sections on Facebook. The results illustrate a broad set of reactions and rationales to Facebook's (public-facing) algorithmic profiling, ranging from shock and surprise, to accounts of how superficial – and in some cases, inaccurate – the profiles were. Taken together with the increasing reliance on Facebook as critical social infrastructure, our study highlights a sense of algorithmic disillusionment requiring further research. [ABSTRACT FROM AUTHOR]

Despite the increasing prevalence of curation algorithms on everyday social network sites (SNSs), they often are imperceptible and difficult to become knowledgeable about since little insight in the actual working of these algorithms is given. To address this, we developed an online interface as a visual feedback tool to decrease the ignorance about the Instagram curation algorithm, named 'Instawareness' (). As such, the goal of this article is to validate the actual effectiveness of Instawareness and to demonstrate how people can be made aware of the Instagram curation algorithm using a quasi-experiment. Validating the actual effectiveness of Instawareness allowed us to connect additional findings about the influence of awareness and understanding of Instagram's curation algorithm to our primary validated findings about achieving such awareness. These show that it is not cognitive understanding about Instagram's algorithms but solely awareness about them that appears to be sufficient for people to indicate increased critical concerns towards SNSs. Furthermore, Instawareness proved to be efficient in increasing cognitive media literacy (CML) and in indirectly stimulating critical concerns towards SNSs. [ABSTRACT FROM AUTHOR]

The ability of citizens to establish the credibility of information and information sources through critical assessment is often emphasized as essential for the upholding of a democratic society and for people's health and safety. Drawing on material-discursive conceptualizations, the article asks, how does critical assessment of information and information sources play out as it is folded into a networked information infrastructure in which different types of information are mediated and shaped by the same algorithms and flattened into the same interfaces? The empirical material comprises dyadic interviews with 61 adolescents. The interviews were analysed using an interpretative approach focusing on the construction of action and meaning. The analysis foregrounds trust and agency as two dimensions. This way normative assumptions become visible as stereotypes, sometimes positioned as ideals towards which to strive, other times as deterrent examples: the non-evaluator, the naïve evaluator, the skeptical evaluator and the confident evaluator. The created stereotypes help to comprehend different understandings of critical assessment of information and how these can bring about different actions. The article argues that critical assessment of information as an element in media and information literacy must be understood not just in relation to how it is used to assess the credibility of information, but also regarding how it is performatively enrolled in the shaping of knowledge and in the creation of ignorance and doubt. [ABSTRACT FROM AUTHOR]

As continual scandals around internet data collection, manipulation and dissemination (the Snowden disclosures, the Facebook emotion contagion research and more recently, the Cambridge Analytica revelations) have made resoundingly apparent, activity online – because it is online – can be mapped, managed and manipulated. How well everyday users understand and manipulate the possibilities, constraints and imperatives of the programmed environments within which they operate may be able to be discerned through a closer examination of actions within the sphere of social game play. We are interested in how gamer awareness of programmed requests to engage, divulge information, connect to other users alongside broader privacy concerns are navigated and translated into specific tactical behaviours and choices. Drawing together results from literature reviews and a qualitative online questionnaire, we discuss the everyday practices of social gamers in their interaction with games as algorithmic, programmed spaces. What is apparent from our discussion is that social games offer a multi-faceted microcosm for a closer analysis of the nuanced interplay of algorithms, data acquisition management and player visibility tactics understood in a broad sense. [ABSTRACT FROM AUTHOR]

The image of big data and algorithms in society is obviously ambivalent. On the one hand, algorithms are seen as a tool of empowerment that allows us, for example, to render society transparent and thus governable, to the extent that the social sciences might even become obsolete. On the other hand, algorithms seem to assume a mysterious agency in the black box of the computer so that their operations are invisible and inscrutable to us: artificial intelligence is seen as something that one day will have the power to dominate us. Beyond these two extreme positions that both overestimate and underestimate how algorithms might change our way of seeing things and being in the world, the present article introduces a third perspective. Algorithms, it holds, indeed follow their own 'style of reasoning' and thus create new realities. At the same time, however, they 'reduce reality', as they lack access to the world of human sense making. Algorithms have no secrets but deploy a 'logic of the surface'. As they paint a behaviorist picture of human modes of existence, algorithms and big data might change our self-understanding. Engaging in epistemological questions will help us to capture the ontological implications of algorithmic reasoning. [ABSTRACT FROM AUTHOR]

Increasingly, artificial intelligent (AI) algorithms are being applied to automatically assist or automate decisions. Such statistical models have been criticized in the existing literature especially for producing cultural biases and for challenging our notions of knowledge. However, few studies have contributed to an essential understanding of the way in which algorithms are designed with particular truths to enable systematic decision-making. Drawing on an ethnographic study in a Scandinavian AI company, this article analyzes how truth is built through layered interpretative practices in applied AI for healthcare, and critically assesses how such practices shed light on the pragmatic notion of truth(s) in AI. The study identifies five practices that all show difficulty in modeling fuzzy patient conditions into one firm truth. The key contribution is that truth goes from being a process of discovering a more 'right' truth to become a process of reinventing the existing truth and healthcare practice. These findings suggest that truth in applied AI is a key devise for making predictive algorithms a viable business, and that developers are in a favorable position to make not only AI doable but also the very truth they intend to find and model. The study in this way shows how change is an inherent part of making AI systems, and that centralizing truth practices is a fruitful way of analyzing such changes and developers' agency. We argue for analytical awareness of how AI truth practices may prompt a world that is fit to algorithms rather than a world to which algorithms are fit. [ABSTRACT FROM AUTHOR]

Big data and data science transform organizational decision-making. We increasingly defer decisions to algorithms because machines have earned a reputation of outperforming us. As algorithms become embedded within organizations, they become more influential and increasingly opaque. Those who create algorithms may make arbitrary decisions in all stages of the 'data value chain', yet these subjectivities are obscured from view. Algorithms come to reflect the biases of their creators, can reinforce established ways of thinking, and may favour some political orientations over others. This is a cause for concern and calls for more transparency in the development, implementation, and use of algorithms in public- and private-sector organizations. We argue that one elementary – yet key – question remains largely undiscussed. If transparency is a primary concern, then to whom should algorithms be transparent? We consider algorithms as socio-technical assemblages and conclude that without a critical audience, algorithms cannot be held accountable. [ABSTRACT FROM AUTHOR]

Social media is an increasingly widely used and important source of news. News on social media is 'selected' by a variety of actors, including the editors and journalists that produce the content, and the algorithms developed by technology companies to make automatic display decisions based on users' past behaviour and the actions of their friends. We analyse how people navigate news on social media, and focus on their perception of the different kinds of news selection involved. The analysis uses a mixed-methods design based on focus group material and survey data from Germany, Spain, the United Kingdom, and the United States. Across all four countries, with their different political and media systems, we find (i) that the majority do not understand exactly how the information they receive is filtered by algorithms, but they do not uncritically accept it either, because they are sceptical of all forms of selection ‒ including that performed by editors and journalists, (ii) that approval for algorithmic selection is stronger amongst younger people, and (iii) that those with a high level of interest in 'soft' news topics (and low interest in 'hard' news topics) are more likely to approve of news algorithmically selected on the basis of what their friends have consumed. We argue that the way in which most people navigate news on social media is thus based on a 'generalised scepticism' where people question all kinds of selection. [ABSTRACT FROM AUTHOR]

In the month before the 2016 U.S. Presidential election, Wikileaks released 37 serialized batches of e-mails authored by former Clinton campaign manager John Podesta. Each release was announced using a unique PodestaEmail related hashtag (#PodestaEmails2, #PodestaEmails3, etc.). In total, Podesta e-mail related hashtags hit town-wide, country-wide, or worldwide Trending topics lists a total of 1,917 times, remaining on Trending Topic lists everyday within the U.S. for 30 days before election day. In this article, we discuss how Wikileaks' release methodology increased the potential reach of Podesta E-mail related content. We describe how Wikileaks' tweets spoke to two audiences: Twitter users and Twitter algorithms. In serializing its content and using new hashtags for each release, Wikileaks increased the potential persistence, visibility, spreadability, and searchability of this content. By creating the possibility for this content to remain persistently visible on the Trending Topics list, Wikileaks was able to potentially realize a greater degree of agenda-setting than would have been possible through singular hashtag use. [ABSTRACT FROM AUTHOR]

Freedom of information laws are intended to illuminate how governments operate. However, the operations of governments increasingly involve algorithms, such as those used to recommend criminal sentencing and determine eligibility for social services. Algorithms function as ‘black boxes’ that turn inputs into outputs using processes that are often, by design, not transparent. Freedom of information laws allow one potential means for algorithmic transparency. However, whether such laws can be used to access algorithms is unclear. This research examines, in two ways, the availability of government algorithms to the public. First, this study examines laws, regulations, advisory opinions, and court rulings relevant to the disclosure of algorithms. The second part of this study analyzes actual responses by US government agencies to Freedom of Information Act requests for algorithms. This study concludes that governmental policies and practices related to algorithmic disclosure are inconsistent. Such inconsistencies suggest a need for better mechanisms to hold government algorithms accountable. [ABSTRACT FROM AUTHOR]

Few studies attempt to demonstrate whether and how systemic racial inequality might form on the web. I use racial formation theory to conceptualize how race is represented, and systematically reproduced on the web, and how both may reveal forms of racial inequality. Using an original dataset and network graph, I document the architecture of web traffic, and the actual traffic patterns among and between race-based websites. Results demonstrate that web producers create hyperlink networks that steer audiences to websites without respect to racial or nonracial content. However, user navigation reflects a racially segregated traffic pattern; users navigate to racialized versus nonracialized websites (and vice versa) more than what would be expected by chance. These results, along with disparities in website traffic rankings, provide evidence of, and demonstrates how a race-based hierarchy might systematically emerge on the web in ways that exemplify disparate forms of value, influence and power that exist within the web environment. [ABSTRACT FROM PUBLISHER]

This article explores the questions associated with what might be thought of asthe social power of algorithms. The article, which introduces a special issue on the same topic, begins by reflecting on how we might approach algorithms from a social scientific perspective. The article is then split into two sections. The first deals with the issues that might be associated with an analysis of the power of the algorithms themselves. This section outlines a series of issues associated with the functionality of the algorithms and how these functions are powerfully deployed within social world. The second section then focuses upon thenotionof the algorithm. In this section, the article argues that we need to look beyond the algorithms themselves, as a technical and material presence, to explore how the notion or concept of the algorithm is also an important feature of their potential power. In this section, it is suggested that we look at the way that notions of the algorithm are evoked as a part of broader rationalities and ways of seeing the world. Exploring the notion of the algorithm may enable us to see how algorithms also play a part in social ordering processes, both in terms of how the algorithm is used to promote certain visions of calculative objectivity and also in relation to the wider governmentalities that this concept might be used to open up. [ABSTRACT FROM PUBLISHER]

Because information algorithms make judgments that can have powerful consequences, those interested in having their information selected will orient themselves toward these algorithmic systems, making themselvesalgorithmically recognizable, in the hopes that they will be amplified by them. Examining this interplay, between information intermediaries and those trying to be seen by them, connects the study of algorithmic systems to long-standing concerns about the power of intermediaries – not an algorithmic power, uniquely, but the power to grant visibility and certify meaning, and the challenge of discerning who to grant it to and why. Here, I consider Dan Savage’s attempt to redefine the name of U.S. Senator Rick Santorum, a tactical intervention that topped Google’s search results for nearly a decade, and then mysteriously dropped during the 2012 Republican nominations. Changes made to Google’s algorithm at the time may explain the drop; here, they help to reveal the kind of implicitly political distinctions search engines must invariably make, between genuine patterns of participation and tactical efforts to approximate them. [ABSTRACT FROM PUBLISHER]

This article examines IBM’s ‘Smarter Education’ program, part of its wider ‘Smarter Cities’ agenda, focusing specifically on its learning analytics applications (based on machine learning algorithms) and cognitive computing developments for education (which take inspiration from neuroscience for the design of brain-like neural networks algorithms and neurocomputational devices). The article conceptualizes the relationship between learning algorithms, neuroscience, and the new learning spaces of the city by combining the notion of programmable ‘code/space’ with ideas about the ‘social life of the brain’ to suggest that new kinds of ‘brain/code/spaces’ are being developed where the environment itself is imagined to possess brain-like functions of learning and ‘human qualities’ of cognition performed by algorithmic processes. IBM’s ambitions for education constitute a sociotechnical imaginary of a ‘cognitive classroom’ where the practices associated with data analytics and cognitive computing in the smart city are being translated into the neuropedagogic brain/code/spaces of the school, with significant consequences for how learners are to be addressed and acted upon. The IBM imaginary of Smarter Education is one significant instantiation of emerging smart cities that are to be governed by neurocomputational processes modelled on neuroscientific insights into the brain’s plasticity for learning, and part of a ‘neurofuture’ in-the-making where nonconscious algorithmic ‘computing brains’ embedded in urban space are intended to interact with human cognition and brain functioning. [ABSTRACT FROM PUBLISHER]

This article reflects the kinds of situations and spaces where people and algorithms meet. In what situations do people become aware of algorithms? How do they experience and make sense of these algorithms, given their often hidden and invisible nature? To what extent does an awareness of algorithms affect people's use of these platforms, if at all? To help answer these questions, this article examines people's personal stories about the Facebook algorithm through tweets and interviews with 25 ordinary users. To understand the spaces where people and algorithms meet, this article develops the notion of the algorithmic imaginary. It is argued that the algorithmic imaginary – ways of thinking about what algorithms are, what they should be and how they function – is not just productive of different moods and sensations but plays a generative role in moulding the Facebook algorithm itself. Examining how algorithms make people feel, then, seems crucial if we want to understand their social power. [ABSTRACT FROM PUBLISHER]

This article introduces and discusses bot-based collective blocklists (or blockbots) in Twitter, which have been developed by volunteers to combat harassment in the social networking site. Blockbots support the curation of a shared blocklist of accounts, where subscribers to a blockbot will not receive any notifications or messages from accounts on the blocklist. Blockbots support counterpublic communities, helping people moderate their own experiences of a site. This article provides an introduction and overview of blockbots and the issues that they raise about networked publics and platform governance, extending an intersecting literature on online harassment, platform governance, and the politics of algorithms. Such projects involve a more reflective, intentional, transparent, collaborative, and decentralized way of using algorithmic systems to respond to issues of platform governance like harassment. I argue that blockbots are not just technical solutions but social ones as well, a notable exception to common technologically determinist solutions that often push responsibility for issues like harassment to the individual user. Beyond the case of Twitter, blockbots call our attention to collective, bottom-up modes of computationally assisted moderation that can be deployed by counterpublic groups who want to participate in networked publics where hegemonic and exclusionary practices are increasingly prevalent. [ABSTRACT FROM AUTHOR]