The emerging field of critical algorithm studies can roughly be grouped into studies that centre on (single) algorithms per se as their unit of analysis, and those that focus on the socio-technical context of AS applications. Studies focusing on the algorithm itself show the capabilities of AS and aim to detect an algorithm’s inner workings, typically by reverse engineering the code (Diakopoulos, 2015), experimental settings (Jürgens, Stark, & Magin, 2015), or code review (Sandvig, Hamilton, Karahalios, & Langbort, 2014). Often, however, they are not able to determine the overall social power that algorithms exert, because algorithms are studied in isolation and user perceptions and behaviour are not sufficiently accounted for. Generally, a purely technical definition of algorithms as encoded procedures that transform input data into specific output based on calculations (e.g., Kowalski’s, 1979, ‘algorithm = logic + control’) and the mere uncovering of the workings of an algorithm do not reveal much about the risks of their applications and their social implications. Algorithms remain ‘meaningless machines’ (Gillespie, 2014) or ‘mathematical fiction’ (Constantiou & Kallinikos, 2015) until they are connected to real-world data (Sandvig et al., 2014). This is accounted for in studies on the socio-technical context of AS, where algorithms are viewed as situated artefacts and generative processes embedded in a complex ecosystem (Beer, 2017; Willson, 2017). As such, algorithms are only one component in a broader socio-technical assemblage (Kitchin, 2017), comprising technical (e.g., software) and human (e.g., uses) components (Willson, 2017). By focusing on internet-based applications that build on algorithmic selection as units of analysis and on the societal functions they perform (see Table 1), this article integrates itself within the second group of research.

The typology in Table 1 demonstrates how broad the scope of AS applications has become. An approach that focuses on socio-technical and functional aspects is accessible for research into the social, economic and political impact of algorithms (Latzer et al., 2016) and the power algorithms may have as gatekeepers (Jürgens, Jungherr, & Schoen, 2011), agents (Rammert, 2008), ideologies (Mager, 2012) or institutions (Napoli, 2014). The institutional governance perspective, that is applied in this paper, identifies algorithms as norms and rules that affect daily behaviour by limiting activities, influencing choices, and creating new scope for action. They shape how the world is perceived and what realities are constructed. In essence, algorithms co-govern everyday life and impact the daily individual construction of realities—the individual consciousness—and consequently the collective consciousness, which in turn makes them a source and factor of social order, resulting from a shared social reality in a society (Just & Latzer, 2017).

The governing role of algorithms needs further analytical specification. As general-purpose technologies (Bresnahan, 2010), algorithms have an impact on a wide range of life domains, and as enabling technologies their impact is contingent on social-use decisions. From a co-evolutionary perspective (Just & Latzer, 2017), algorithmic governance is a complex, interconnected system of distributed agency (Rammert, 2008) between humans and software, a co-evolutionary circle of permanent shaping and being shaped at the same time. Algorithms co-govern what can be found (e.g., algorithmic searches), what is anticipated (e.g., algorithmic forecasts), consumed (e.g., algorithmic recommendations) and seen (e.g., algorithmic filtering), and whether it is considered relevant (e.g., algorithmic scoring) (Just & Latzer, 2017). They thereby contribute to the constitution and mediation of our lives (Beer, 2009). The use of only vaguely defined terms like algorithmic decision-making can be misleading regarding the assessment of social consequences of different kinds of algorithmic governance. Various analytical distinctions should be kept in mind when studying algorithmic governance:

Algorithmic selection applications on the internet differ widely in their degree of automation and autonomy. At one end of the spectrum, algorithms are used as instruments with imposed agency to exert power without any autonomy, with predefined and widely predictable outcomes

e.g., simple alphabetical sorting.

To indicate the actual autonomy of algorithmic systems on the internet, a similar classification to that applied for self-driving cars may be helpful, where a labelling from 1 (low) to 5 (full) marks the degree of automation (Bagloee, Tavana, Asadi, & Oliver, 2016). Literature on automated weapons systems provides another instrumental way to categorise the remaining control by humans in automated decision-making systems: humans are classified as being either (1) in-the-loop and fully in control, (2) on-the-loop and able to intervene if felt necessary, or (3) off-the-loop and without any option to intervene (Citron & Pasquale, 2014). This distinction, for example, proves helpful when liabilities for algorithmic governance are evaluated. The term automated decision-making algorithms often refers to decisions by algorithms without human involvement (off-the-loop), and has already led to regulatory interventions. The use of automated decision-making systems with significant legal or social effects is restricted (e.g., fully automated tax assessments), for example, by article 22(1) of the European General Data Protection Regulation (GDPR), whereas the use of other automated decision-making systems—based on non-personal data—is not restricted (Martini & Nink, 2017).

Algorithmic selections as part of internet-based applications are related to everyday human decisions in different ways. In most of the functional categories listed in Table 1, automated algorithmic selections are applied to augment and enhance everyday human decision-making but not to fully replace it. This is predominantly the case for algorithmic recommendations, filtering and scoring results. Nevertheless, it has to be considered that in many cases (e.g., credit scoring, predictions on recidivism, ranking of job candidates) it becomes increasingly problematic for those responsible to ignore or counteract algorithmic results in their decisions, in particular if these algorithmic outputs are accessible to others or to the public. Accordingly, AS applications that are aimed at enhancing human decisions can de facto evolve into systems where humans merely remain on-the-loop and will only intervene in exceptional cases.

Further, algorithmic selections vary strongly in their scope of potential consequences (social and economic risks). For instance, there is a significant difference between a simple algorithmic filtering concerning which post from a friend is shown in someone’s social media feed and a more meaningful and directly relevant algorithmic scoring of someone’s creditworthiness. Accounting for the case-specific scope and context of algorithmic selections is therefore highly relevant for appropriate policy conclusions. For instance, two technologically identical algorithms where one is applied for recommending books and the other for recommending medical treatments call for very different policies due to the disparity of risks of these automated algorithmic selections.

Algorithmic (co-)governance results in opportunities and risks. The advantages of algorithmic governance such as efficiency gains, speed, scalability and adaptability are compromised by risks ranging from bias, manipulation and privacy violations, to social discrimination, heteronomy and the abuse of market power (Latzer et al., 2016), or by efficiency-based (inaccurate decisions) and fairness-based objections (unfair decisions) in algorithmic governance (Zarsky, 2016).

In sum, while algorithms are increasingly active as tools and actors in governance regimes that affect many life domains on a daily basis, the relative importance of algorithmic governance is far from clear. The practice-related approach proposed here aids the empirical assessment and understanding of this significance of algorithmic governance.

Everyday life as a field of research is rooted in various theoretical traditions (Adler, Adler, & Fontana, 1987), among other things in phenomenological sociology (Schütz, 2016), historical materialism (Heller, 1984) and De Certeau’s (1984) anthropology.

As for the area of inquiry, this paper takes a practice-related approach (Pink, 2012). Since the field lacks comprehensive empirical research that goes beyond individual services, this article suggests studying the significance of algorithmic governance for everyday life in a more inclusive manner. In order to derive an executable research design, however, it is necessary to analytically segment ‘everyday life’. We focus on four domains of everyday life that span central areas of everyday practice: (a) social and political orientation, (b) recreation, (c) commercial transactions, and (d) socialising. This categorisation is derived from a representative, country-wide CATI survey of internet use in Switzerland. While an infinite number of activities can be performed on the internet, a confirmatory factor analysis revealed four distinct internet usage factors that group the most important internet activities for Swiss internet users (see Büchi, Just, and Latzer, 2016 for an overview of the activities for each domain). Therefore, this categorisation lends itself to an analytical distinction between different life domains in which people engage in online activities and use AS applications in particular. It is important to note that these life domains are obviously closely interrelated and do not necessarily represent the categories in which individuals perceive their everyday lives. Although there is no standard conceptual framework for everyday life, Sztompka (2008), for example, points to its various defining traits, such as that everyday life events include relationships with other people, that they are repeated and not unique, have a temporal duration, and often happen non-reflexively, following internalised habits and routines.

In order to appropriately account for the increasing role of technology, research must go beyond human relationships as one defining characteristic of everyday life. The theory of the social or mediated construction of reality (Berger & Luckmann, 1967; Couldry & Hepp, 2016) is fruitful for the understanding of how social interactions and media technologies shape the perception of the social world. Berger and Luckmann (1967) argue that the social world is constructed through social interactions and underlying processes of reciprocal typification and interpretation of habitualised actions. In this meaningful process, a social world is gradually constructed whose habitualised actions provide orientation, make it possible to predict the actions of others and reduce uncertainty. This leads to an attitude that the world in common is known, a natural attitude of daily life (Schütz & Luckmann, 2003). Accordingly, the resources, interpretations and the common-sense knowledge of routinised practices in everyday life—which increasingly includes AS applications—are seemingly self-evident and remain unquestioned.

This paper particularly aims to expose what is generally left unquestioned and to propose a guideline for the assessment of perceptions and use of AS applications for a wide range of everyday practices in order to better understand their impact, associated risks, and the need for public policies. Willson (2017) emphasises that one of the concerns of studying the everyday is to make the invisible visible and to study the power relations and practices involved. AS applications are seamlessly integrated into the routines of everyday life through domestication (Silverstone, 1994)—the capacity and the process of appropriation—which renders them invisible. Algorithms operate at the level of the ‘technological unconscious’ (Thrift, 2005) in widely unseen and unknown ways (Beer, 2009). Consequently, the study of algorithms aims to reveal the technological unconscious and to understand how AS applications co-govern everyday online and offline activities. AS applications must be investigated in relation to online and offline alternatives to determine the relative significance of algorithmic governance for everyday life, for example by bearing in mind an individual’s media repertoire

Consideration of individuals’ entire media repertoires, comprising online and offline sources, is vital because, for instance, the effects of using AS services like Facebook for news purposes vary with the person’s use of other news channels or other (offline) sources.

(a) The significance of algorithmic governance has received the most attention in research on social and political orientation. Search applications and news aggregators are understood as intermediaries (Bui, 2010; Newman, Fletcher, Kalogeropoulos, Levy, & Nielsen, 2018) between traditional mass media and individual news consumption. Empirical research suggests that algorithmic selection will become more important for information retrieval in the future (Newman et al., 2018; Shearer & Matsa, 2018). Accompanying these considerations are fears of personalised echo chambers (Sunstein, 2001) or filter bubbles (Pariser, 2011), leading to fragmented, biased perceptions of society (Dylko, 2016). However, recent empirical studies fail to show a coherent picture: there are clear patterns of algorithmically induced, homogenous opinion networks (Bakshy, Messing, & Adamic, 2015; Del Vicario et al., 2016; Dylko et al., 2017), but other studies indicate more opinion diversity despite algorithmic selection and qualify the risk of echo chambers with empirical evidence (Barbera, Jost, Nagler, Tucker, & Bonneau, 2015; Dubois & Blank, 2018; Fletcher & Nielsen, 2017; Heatherly, Lu, & Lee, 2017; Helberger, Bodo, Zuiderveen Borgesius, Irion, & Bastian, 2017; Zuiderveen Borgesius et al., 2016).

(b) AS applications also increasingly shape daily recreation (i.e., entertainment and fitness). Recommendation applications have been shown to play a predominant role here. The main concerns are diminishing diversity (Nguyen, Hui, Harper, Terveen, & Konstan, 2014), the algorithmic shaping of culture (Beer, 2013; Hallinan & Striphas, 2016) and the social power of algorithms (Rieder, Matamoros-Fernandez, & Coromina, 2018). Again, there has been no clear empirical evidence for this hypothesis, but rather studies qualifying this risk (Nguyen et al., 2014; Nowak, 2016).

Further, wearables—networked devices equipped with sensors—have entered everyday life. Empirical studies investigate the perception, use and modes of self-tracking (Lupton, 2016; Rapp & Cena, 2016), and its social and institutional context (Gilmore, 2015). Such wearables have often been disregarded in critical algorithm studies, although they are an important way in which AS governs the perception of the self (Williamson, 2015) and everyday life in general.

(c) For commercial transactions, there has been a focus on studying recommender systems focusing on the performance of algorithms (Ur Rehman, Hussain, & Hussain, 2013) or the implementation of new features (Hervas-Drane, 2015). Their impact on consumers is mostly studied by evaluating their perceived usefulness (Li & Karahanna, 2015). Furthermore, allocation algorithms in the form of online behavioural advertising have attracted attention (Boerman, Kruikemeier, & Zuiderveen Borgesius, 2017), revealing inconsistent results on users’ perceptions of personalised advertisements (McDonald & Cranor, 2010; Smit, Van Noort, & Voorveld, 2014; Ur, Leon, Cranor, Shay, & Wang, 2012).

(d) For socialising, the research focus is on how algorithms curate user interactions on social networking sites and dating platforms (Bucher, 2012; Hitsch, Hortaçsu & Ariely, 2010). These applications raise concerns like social distortion effects or the question of how social connections are adapting to an algorithmically controlled model (Eslami et al., 2015; Rader, 2017; Rader & Gray, 2015; Van Dijck, 2013). So far, there has been no empirical analysis to confirm the relevance of these risks.

Altogether, research on the impact of algorithmic governance on everyday life has produced a plethora of theoretical considerations and fragmented, application-specific empirical findings. To date there has been no comprehensive and systematic empirical investigation of the various central domains of everyday practices. However, generalising policy implications from studies on individual AS services (e.g., Facebook, Twitter or search engines) should be treated with caution. Moreover, existing studies focus on AS applications in relative isolation. Due to this narrow perspective, they are unable to evaluate the power of algorithmic governance in everyday life. Existing work has mostly taken a top-down approach, disregarding the perspective of users. Studies on user perceptions have predominantly relied on self-reported survey measures. While extensive qualitative studies (e.g., Bucher, 2017) offer the basis for a better scientific understanding of the social effects of AS applications, they do not allow generalisable statements at the population level. There is also a lack of empirical work with data on individuals’ actual internet use. To the best of our knowledge, there is no empirical study on the population level that uses tracking data on both mobile and desktop devices, a prerequisite to gain a comprehensive picture of individual internet use. Finally, there have been very few nationally representative studies on the use and perception of AS (e.g., Araujo et al., 2018; Fischer & Petersen, 2018). These existing empirical results do not provide a sound basis for policy-making in this area.

The following section proposes a methodological design that is suited to filling the research gaps identified above. It is designed with the objectives of providing a better understanding of how algorithms exert their power over people (Diakopoulos, 2015)—which essentially corresponds to our understanding of algorithmic governance—and to offer useful evidence-based insights for public policy deliberations regarding algorithmic governance and the policy choices for the governance of algorithms.

To empirically grasp the significance of algorithmic governance for everyday life, we develop a theoretical model that accommodates the operationalisation of algorithmic governance and entails five variables that influence the potential and effectiveness of this particular type of governance: usage of AS applications, subjective significance assigned to them, awareness of AS, awareness of associated risks, and practices to cope with these risks.

First, in order to determine the governing potential of AS applications in everyday life, their usage (extent, frequency) must be measured, particularly compared to their online and offline counterparts. Also, their governing potential is determined by whether and how these applications have changed people’s behaviour, for instance with regard to individual information seeking, listening to music, gaming, or dating. Second, the subjective significance people attribute to these applications plays an important role in how AS applications affect everyday life. The substantial substitution of traditional online and offline alternatives by AS applications is a prerequisite if fears of AS-associated risks are to be justified. Assessing the significance that users assign to AS applications makes it possible to determine the accuracy of these theoretical estimations. Third, it is essential to investigate how aware people are of the fact that algorithms operate in the services they use and of the specific algorithmic modes of operation. Awareness of AS substantially affects the effectiveness and impact of algorithmic governance. A variety of risks is attributed to the use of AS applications (e.g., filter bubbles, diminishing diversity of content), which are often directly associated with the algorithmic modes of operation. Accordingly, without awareness, users cannot accurately assess potential benefits and risks

Awareness is not to be misunderstood as knowledge of specific algorithmic modes of operation here. Our model suggests that, for instance, without being aware that Google search results are personalised, individuals can not grasp the concept of filter bubbles. They are therefore unable to understand this risk and maybe adapt their behaviour accordingly.

Suitable assessments of risks related to AS applications and corresponding policy measures require the empirical measurement of the governance that AS applications exert in users’ everyday lives. To answer the call for taking algorithms’ ‘socio-technical assemblages’ (Kitchin, 2017) into account and investigating how users engage with AS applications in their lives, existing top-down approaches should be complemented by a user-centred perspective (Bucher, 2017).

Therefore, we propose a user-centred, mixed-methods approach to measuring the significance of AS applications, which is comprised of three research phases. Based on a literature review, (I) semi-structured qualitative interviews are to be conducted for each of the four domains of everyday practice. As these practices (e.g., newsgathering, dating) are not limited to internet use, the significance of AS applications must be considered in relation to alternative online and offline activities. This enlarged and contextualised perspective promises to provide an understanding of individuals’ life worlds and how AS applications are integrated within them. The qualitative interviews can provide in-depth information on individuals’ perceptions, opinions and interpretations regarding AS applications in the four life domains.

These qualitative interviews should form the basis for the quantitative empirical part, which we propose to consist of a representative online survey (II) in combination with a representative passive metering (tracking) (III) of internet usage at the population level. The combination of self-reported survey measures and tracked internet use (passive metering) makes it possible to compare the tracked share of AS services used with the self-reports of internet use, which can be systematically biased (Scharkow, 2016) or subject to social desirability effects. Further, the non-transparent, “black-box” nature of algorithms raises questions about users’ awareness of the mechanisms at play. When asking people about their experiences with algorithms, it must be kept in mind that their awareness of the existence of algorithms might be low and their statements could be biased accordingly. Therefore, a measurement of AS by means of tracking data additionally to the interview and survey data is inevitable

Tracking data can also be subject to different biases (e.g., self-selection biases), which must be considered when applying these novel methods (see e.g., Jürgens, Stark, & Magin, 2019).

All three methodological approaches lend themselves to the accomplishment of different goals and results, which are summarised in Table 2. Only in its entirety is this mixed-methods approach able to significantly contribute to closing existing research gaps with regard to the empirical understanding of algorithmic governance and the overall significance of AS applications in everyday life.

This mixed-methods approach allows for a re-assessment of opportunities and risks of AS applications in the different life domains that form the basis for evidence-based public policy and governance of AS applications, aiming at the democratic control of algorithmic power. The guideline that we propose is to be understood as an exemplary research design that has to be adapted to specific research questions

This guideline – combining the proposed theoretical model and mixed-methods research design – has already been applied by the authors in Switzerland. Results from qualitative internet user interviews and a representative online-survey combined with internet use tracking on a mobile and desktop device for a representative sample of the Swiss population are forthcoming.