**1. Introduction**

In most parts of Central Tunisia, and particularly in Haffouz and BouHafna regions, groundwater has played a fundamental role in shaping the social and economic development.

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Due to rapid demographic growth and agricultural progress, exploitation of groundwater has increased dramatically to provide drinking water to rural community, support irrigation, and maintain ecosystems. During the past 30 years, the exponential increase in water abstraction from Haffouz and BouHafna aquifers has caused ground water depletion, a term often defined as long-term water-level declines, and an increase in salinity of groundwater pumped from wells situated mainly downstream. On the other hand, recent changes in agricultural land use and irrigation may result in groundwater contamination throughout agricultural fertilizers and pesticides applied to fields. Therefore, information about irrigation groundwater quality is critical to the understanding of necessary management changes for long-term productivity [1]. Proper assessment of groundwater sustainability requires understanding and quantification of human effects on water resources using analysis, application of management practices, and revision.

Within this framework, a combined hydrogeological and hydro-chemical data were examined, using statistical methods, to determine (i) natural and anthropogenic processes that control the groundwater mineralization; (ii) the origin of different water bodies and their sources of recharge; and (iii) to assess the suitability of groundwater for agricultural purposes.


Secondly, the work of our algorithms provides us with an opportunity to review the complexities of morality and vision in demonstrations in new ways. Previously, Smith (2009) has suggested a complex relationship between originals, fabrications and partial fabrications in viewing the staged drama of a demonstration and Coopmans (2010) has argued that revelation and concealment are central to the practices of seeing in demonstrations. These are important contributions, and through our algorithms we can start to note that what the technology sees (e.g. the algorithms turn out to be able to see certain types of non-refective fooring better than others) and the distribution of vision (who and what sees) and the organisation of vision (who and what is in a position to produce an account of who and what), are important issues in the integrity of demonstrations. The train station demonstration can have more or less integrity according to this distribution and organisation of vision. If recorded footage is used but the algorithms do not know what it is they will see, this is noted by project participants as having more integrity than if recorded decision-making by the algorithms is also used. In the event both types of recording were used. Discussions in project meetings around the demonstration for project funders, led to similar questions The algorithms need to see correctly (in classifying luggage as luggage-shaped objects) and to be seen correctly seeing (in producing system results) by, for example, project funders and ethical experts, in order to accomplish the visual-moral integrity to which the project has made claim: that the algorithms can grasp everyday life.

### Conclusion

In this chapter, the focus on demonstrating our algorithms' ability to grasp everyday life, compose accounts of everyday life and become the everyday of the airport and train station, has drawn attention to notions of integrity. Given the project's ethical aims, work to bring a world into being through demonstration can be considered as concerted activities for bringing about a morally approved or better world. The moral terms of demonstrations can thus go towards establishing a basis from which to judge their integrity. Close scrutiny of demonstration work can then open up for analysis two ways of questioning the integrity of the moral world on show. Through material integrity, questions can be asked of the properties of demonstrations, what they seem to be and how they indexically provide for a means to constitute the moral order to which the demonstration attests. Through visual-integrity questions can be posed of who and what is seeing, the management of seeing, what it means to see correctly, and be seen correctly. Material and visual integrity is managed in such a way as to allow for the demonstrations to produce a relation of undoubted correspondence between index and context, establishing the integrity of the material and visual features of the technology: that it sees and has been seen correctly, and that the acts of seeing and those doing the seeing can be noted as having suffcient moral integrity for those acts of seeing to suffce.

The problems our algorithms have with grasping and composing an account of the everyday life of the airport and train station require that this integrity is compromised. Just taking Fig. 5.3 as an example, the human-shaped object composed by the algorithm does not match the human in the real time and real space of the airport (the system has placed a bounding box only around the human's head). Algorithmic time and space has produced a mismatch with airport time and space; the everyday life of the algorithm and the airport are at odds and the relation of undoubted correspondence between algorithmic index and airport context does not have integrity; the algorithm's composition of, rather than grasping of, the human is laid bare. A compromise is required to overcome this mismatch. Years of work to grasp the human-shaped object (Chapter 2), to make that grasping accountable (Chapter 3) and to demarcate relevance from irrelevance (Chapter 4) are all now at stake. In the next Chapter, we will see that our algorithms' problems in seeing and the need to adopt these compromises, poses questions for composing and managing the market value of the technology.

### Notes

http://www.theguardian.com/uk/2008/may/05/nationalarchives.secondworldwar http://news.bbc.co.uk/1/hi/education/1039562.stm; http:// www.timeshighereducation.co.uk/news/fake-verifable-degrees-offered-on-internet/167361.article; and http://www.badscience.net/2008/11/hot-foulair/#more-824.

### References

#### 122 D. NEYLAND

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/ by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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# Market Value and the Everyday Life of the Algorithm

**Abstract** The fnal chapter explores how a market can be built for an algorithmic system. It draws together studies of algorithms with the growing literature in science and technology studies (STS) on markets and the composition of fnancial value. It uses performativity to explore market making for algorithms. To accomplish market work and build a value for the algorithm, the chapter suggests, the project coordinators had to build a market of willing customers who were then constituted as a means to attract others to (potentially) invest in the system. This fnal chapter will suggest that market work is an important facet of the everyday life of an algorithm, without which algorithmic systems such as the one featured in this book, would not endure. The chapter concludes with an analysis of the distinct and only occasionally integrated everyday lives of the algorithm.

**Keywords** Market making · Market share · Investment · Value · Performativity

## Opening

In Chapter 5, the ability of our algorithms to grasp and compose everyday life in the train station and airport came under signifcant scrutiny. Problems in classifying objects and their action states, issuing alerts and demarcating relevant from irrelevant footage were major concerns for the project participants. This built on the problems experienced in Chapter 4 with the deleting machine that seemed to always leave behind orphan frames. Taken together, this suggests that our algorithms might struggle to become the everyday of the airport and train station at least in their current form. The system architecture, the individual components, the relevancy detection algorithms, the IF-THEN rules might all need more work. And for the computer scientists from University 1 and 2, this was no more or less than they expected: their work in this project built on a decade of research carried out by themselves and colleagues that would extend beyond the fxed time frame of this project into future efforts. Our algorithms might live on in modifed form in whatever the computer scientists or other colleagues chose to do next.

The project coordinators faced a different question. For the coordinators of the project—a European-based consulting frm—the possibilities of developing an ethical, algorithmic surveillance system to take to the market, had provided a compelling reason for their involvement in the project. Deletion, relevancy detection and algorithmic experimentation each had a partial orientation for the coordinators towards a future market. Building a value for the technology following trouble with relevancy detection, object classifcation, object tracking, background subtraction, the issuing of alerts and the deletion system appeared challenging. The coordinators instead looked to switch the basis on which the future of the technology was settled. Recognising that the system's results in demonstrations to end users (see Chapter 5) and the deletion system's audit log (see Chapter 4) would generate a continuing output of demonstrative partial failure, the coordinators instead sought to build an alternative basis for relations with the world beyond the technology. This set of relations would seek to map out a new market value for the technology. In place of technical effcacy as a basis for selling the system, willing customers were constituted as a means to attract others to (potentially) invest in the system. In this chapter, I will suggest that building a world of (potential) customers to attract investors required a broad number of participants, with market trends, sizes and values separated out and made subject to calculation. To do market work and build an investment value required this careful plaiting of relations. I will suggest that the efforts required to shift the focus from technical effcacy to investment can be considered through ideas of performativity.

The chapter will begin with a brief digression through recent writing on performativity, before looking at the coordinators' work to draw investors into new relations with the algorithmic system. I will suggest that these relations operated in a similar manner to the object classifcation of our algorithms: investors, territories, future sales and market size had to be separated out and qualifed, calculated and pacifed in order that these new relations of investment might be developed. The chapter will end with a discussion of where we have reached in the everyday life of our algorithms.

### Performativity

Performativity has played an important part in the recent science and technology studies (STS) turn towards markets and marketing (see, for example, MacKenzie et al. 2007; MacKenzie 2008). The argument draws on the work of Austin (1962) and his notion of a performative utterance or speech act. Cochoy (1998) suggests a performative utterance can be understood as a statement 'that says and does what it says simultaneously' (p. 218). MacKenzie suggests a distinction can be made between utterances that do something and those that report on an already existing state of affairs (2008: 16). The most frequently quoted example, drawing on the work of Austin (1962), is the utterance 'I declare this meeting open'. Such an utterance is said to describe and bring into being the state that it describes—it is a speech act.

Developing this further, Cochoy (1998) suggests: 'a performative science is a science that simultaneously describes and constructs its subject matter. In this respect, the 'performation' of the economy by marketing directly refers to the double aspect of marketing action: conceptualising and enacting the economy at the same time' (p. 218). From this, we could understand that marketing brings the matter it describes into being. For other STS scholars, the focus is attuned to markets rather than marketing. For example, Callon suggests: 'economics in the broadest sense of the term performs, shapes and formats the economy' (1998: 2). Araujo thus suggests that performativity involves market and marketing type statements making themselves true by bringing into being the subject of the statement (2007: 218).

In relation to fnancial markets, MacKenzie looks at the ways in which the work of economists brings markets into being. MacKenzie (2003) suggests that traders use market models to inform their trades, creating market outcomes that match the models. Furthermore, economists' market equations embody a world of relations, prices and outcomes that the use of an equation effectively constitutes. The work of economists can be understood in a similar manner to a Kuhnian problem–solution exemplar; the complexity of the world can be rendered more or less coherent through models and equations which appear to work (i.e. to bring a solution to a problem) and can thus be employed again in other similar situations. The models and equations become paradigmatic couplings of problems and solutions for others to use. As a result, the risks faced by market actors in an otherwise complex, messy and uncertain world become reconceptualised as more or less manageable.

However, MacKenzie suggests that performativity is not a uniform phenomenon; instead he presents three approaches to performativity. First, there is 'generic' performativity in which: 'an aspect of economics (a theory, model, concept, procedure, data set, etc.) is used by participants in economic processes, regulations, etc' (2008: 17). Second, there is 'effective' performativity which involves: 'the practical use of an aspect of economics that has an effect on economic processes' (2008: 17). Third, drawing on the work of Barry Barnes, there is 'Barnes-ian' performativity in which: 'Practical use of an aspect of economics makes economic processes more like their depiction by economists' (2008: 17), and actions change in order to 'better correspond to the model' proposed by economists (2008: 19). We can see these approaches to performativity as moving from weakly formulated to more thorough forms of performativity. However, MacKenzie is clear that such models of performativity do not only operate in one direction. MacKenzie also introduces 'counter performativity' whereby: 'practical use of an aspect of economics makes economic processes less like their depiction by economists' (2008: 17).

Although this provides a provocative set of ideas for thinking through how market value for the algorithmic system might be built, performativity has been critiqued for buying too readily into, or merely confrming, the terms of market participants (Riles 2010; Dorn 2012; Bryan et al. 2012; Foucarde 2007; with a response from MacKenzie and Pardo-Guerra 2013). For Lee and LiPuma: 'The analytical problem is how to extend what has been a speech act-based notion of performativity to other discursively mediated practices, including ritual, economic practices, and even reading' (2002: 193). To switch attention to economic processes requires an expansion of the remit of performativity and a rethinking of the centrality of communication (such as Austin's utterances) and an incorporation of acting and doing. Incorporating this broader set of entities would move us towards an approach developed by Barad (2003) who suggests shifting performativity away from its starting point in studies of language use and questions of representation, towards action (a similar extension is proposed by Butler 1997, 2010).

Although Barad is not focused on markets and forms of economic exchange in her discussion of performativity, the questions she raises appear to resonate with concerns posed to the STS move to engage with markets, calculation and measurement; that performativity might problematically narrow the focus for analytical action. Callon's (2006, 2010) response to the critiques of performativity is that they continue (what he suggests is) Austin's (1962) mistake of assuming statements are in some way separable from their social, cultural or political context. Instead, Callon argues for a need to explore the worlds performed into market action. This will be the starting point for our exploration of the project coordinators' market work: just how do they perform a world of investment into being and what does this tell us of the everyday life of our algorithms?

### Building a Market Value for the Algorithms

In the absence of reliable evidence of technical effcacy and given the apparent diffculties of putting on a convincing demonstration of the algorithms' ability to grasp or compose everyday life, the coordinators drew together a variety of entities to participate in the building of a putative world into which investors could be invited. Building such a world was a complex task requiring calculative dexterity in order to render the emerging world convincing and legible in a document that could be sent to investors. It also required imagination to conjure the entities to be calculated and a compelling narrative into which they could be woven. Still this would be nothing more than a putative world of potential investment. For it to be given performative effect required buy-in from the investors.

First, complex, dextrous and imaginative preparation work took place. The project coordinators segmented the world into geographical regions to be accorded more value (Central and South America with strong predicted growth rates in video-based surveillance), even more value (Canada and Europe with a growing interest in video-based surveillance and a burgeoning privacy-interested legislature and lobby) or less value (the USA with apparently less interest in privacy and a saturated market place for smart video analytics). These segmented geographies were not left as vaguely valued territories, but transformed into specifc and precise calculations of Compound Annual Growth Rates (CAGRs) derived from a combination of expensive industry reports the coordinators had purchased and online sources. In this way, the market for video-based surveillance analysis was calculated to have a CAGR of 15.6% between 2010 and 2016. This was then broken down into the more and less attractive geographical segments previously described.

This provided a very hesitant initial set of calculations on which to build an investment proposition: geographies were segmented and calculated. However, this dextrous and imaginative work to separate and calculate did not end here. Customers were treated in much the same way. Hence governments were identifed as a particular type of customer, tied to more or less attractive geographies. The more attractive governments were calculated as accounting for 17.59% of the video surveillance market and as more likely to be compelled into buying a deletion technology in order to promote their own privacy sensitive credentials. Transport frms were another customer type segmented and calculated as accounting for a further 11% of the video surveillance market with a predicted CAGR of 13.39% between 2010 and 2016. Major transport-based terror attacks were invoked as a basis for this growth in investment, but transport organisations were also identifed as another potentially privacy-concerned customer (this despite the transport companies involved in this project seeming to lose interest in privacy as the project developed). Specifc technological developments were also given the same treatment, with pixel numbers, high defnition cameras and algorithmic forms of data analysis all separated and calculated as growth areas. Finally, video-based surveillance processes such as data storage were also separated out and calculated as a growth area, but with a growing storage cost the kind of cost that could be reduced through deletion. Although this separation and calculation work was directed towards building a putative world into which investors might become enwrapped, the coordinators also worked to distinguish entities as outside or external to this world of potential relations. Hence 44 competitors were also identifed, ranked according to size and spend, and their particular video-based, algorithmic data analysis systems were presented in terms of their inferior capabilities. This despite our algorithms continually running into problems.

The work here by the coordinators was similar to that carried out by our algorithms. Separating out, calculating, preparing and qualifying some entities while disqualifying others (such as competitors), grasping features of the world out there and bringing them to the system, provided the basis for building a potential world of investment relations. Alongside segmented geographies, everything from governments to pixel numbers became entities of this putative market work. The entities segmented and qualifed (and disqualifed) were drawn together into the world of relations in a document entitled 'The Exploitation Report'. Here the qualifed (and disqualifed) entities made sense as providing a basis for investment. At the centre of this world of relations, however, sat our algorithms, the system architecture, its components, and the deleting machine as an investment vehicle whose technical effcacy remained absent from accounts. Technical capabilities remained silent, rendering the Report's content accountably certain and ordered. The preparatory calculations embedded in the Report and the censure of any uncertainty in terms of the demonstrable proof of technical effcacy would now provide the basis for performatively accomplishing an effect: building a world of investors. Through convincing investors that the Report was compelling proof of the viability of investment and that the technological system qualifed as a reasonable investment risk, the coordinators hoped to also build investors into the world of the algorithms.

Inclusions, exclusions and careful calculation provided the means for the coordinators to try and build a compelling narrative that would achieve this performative effect. Rather than relying on a single utterance (as in Austin's illustrative examples of performativity), accomplishing this effect relied on the Report's extended narrative as a means to provide a particular kind of evidence (not of technical effcacy, but of investment potential) on a particular scale (across industries and geographies). In place of uncertainty derived from 44 competitors came the assertion that none of the competitors could deliver as sophisticated a solution as that promised by the project. In place of a concern with governments cutting budgets in times of austerity came the assertion that governments must look to cut costs and therefore should look for the kind of cheap storage solutions that auto-deletion technologies could provide. In place of a concern that a new surveillance system might attract privacy-based criticism came the assertion that this system carried with it and provided a response to that privacy criticism. And in place of any concern from among project members that the technology didn't work came nothing; technological inadequacies were excluded from the Report and its audience. Building this compelling narrative (Simakova and Neyland 2008) was central to accomplishing the performative effect.

From the preceding analysis, we can see that our algorithms are not left to fend for themselves, abandoned as a result of their technical ineffcacies. Neither are they exactly excused from any further role in the project. They are in the Exploitation Report, but their lack of effcacy is excluded. To accomplish the performative effect, they need to be present as an investable entity, at the same moment as key features of their activity are absent. The orderly world of the investment proposition is as much dependent on these absences as the presence of the algorithms. Understanding performativity is not then restricted to single speech acts or even the content of the Exploitation Report alone, but requires understanding the concerted efforts to segment, calculate, and prepare a world of people, things, processes, resources and relationships that the investors can enter. Preparing the putative world for investors involved these presences and absences, but also the possibility of accumulating something further. This built on the segmentation, calculation and preparation work to narrate future returns on investments from building an ethical, algorithmic surveillance system. The system could be invested in and might go on to do the work that might be required of companies in the emerging and changing Data Protection and privacy landscape where such matters as a right to be forgotten (see Chapter 4) have gained momentum. Complying with policy requirements and customer expectations of privacy, and delegating this compliance to our algorithms (or at least, future renditions of our algorithms), might become a marketable good and attain a value.

Following many weeks of labour by the project coordinators in producing 'The Exploitation Report', the preparation work of segmentation, calculation and absenting of certain forms of data (on technical effcacy) was hidden. Making sense of the performativity through which an investment proposition is given effect requires an understanding of this preparatory work, but also cannot ignore the compelling narrative in which it is subsequently involved. Market value here achieves its potential through the segmentation of geographies, technologies, competitors and customers, the apportioning of a calculative value (or non-value) to these entities and evidence from third parties to support the values evidenced. This work is only partly evident in the Report. The outcomes rather than the means of calculation, for example, are made prominent. However, the Report itself also needs consideration. The preparation work to segment, calculate and value entities had to be drawn into a compelling narrative that supported the future development of the algorithmic system. Work was thus done to connect things we all know are happening now (such as government austerity measures and the need to cut budgets) with features of the technological future (such as deletion), to generate a compelling narrative for investment in the algorithmic technologies (in this instance, that austerity measures and cost-cutting could be achieved through deletion by cutting data storage costs). And other things that we know are taking place (such as the introduction of the EU General Data Protection Regulation) could be connected with a range of required activities (compliance with the legislation) that could be accomplished via our algorithms. Certainty in the narration of problems (that these problems exist and will be faced by these customers) and solutions (that this system will address these problems) might prove compelling. At the same time, producing a compelling narrative also required that some numbers (technical effcacy) and forms of calculation (how the world of the Report was prepared) remained absent. This continual switching between temporalities—the world as we know it now and the investable future—and accounts—things to be made available and things to be absented—became the means to attempt to compel investors to join the world of relations being built into the algorithmic system; that its market value would arrive.

### The Everyday Life of the Algorithm

Where does this leave our algorithms? As the slightly embarrassing and incapable project partner to be excluded from fnancial calculations, a waste of time and money? And what does this tell us about the drama played out in current academic writing and in the media (see Introduction), in which algorithms are expected to take over our lives, run wild with our data or operate in ways that we cannot see? To address these questions, we need to step back and take a look at the everyday lives of our algorithms as they have developed throughout the chapters of this book. We need to see just where our algorithms have got to in life to make sense of their proposed future, their social, economic and technical prospects.

In the Introduction, we met the abandoned luggage algorithm and its IF-THEN rules. Little more than a set of step-by-step instructions that set out some conditions and consequences, these rules seemed far removed from the drama of artifcial intelligence, big data and the opaque and inscrutable algorithm. Indeed scrutinising these IF-THEN rules appeared to offer little prospect of a great step forward. They were not about to leap off the page and create great change in the world. In order to understand this algorithm and the drama in which it was expected to participate, we needed to get close to its everyday activity. We needed to know just how this algorithm participated in everyday life, grasped or even composed everyday life and participated in the production of effects. We needed to know something about its prospects of becoming the everyday. It seemed clear that the IF-THEN rules alone would have little consequence. We needed to know who and what the algorithms were working with. Rather than treat the non-human as an incidental fgure (as much of the sociological writing on the everyday has tended to), the algorithm would be accorded a specifc kind of status. As a frst move, we needed to de-centre the human as we know it from the middle of the drama. We could not afford to assume that this was primarily a story to be told by people. We needed to give the algorithm and its technical partners, at least in principle, the same potential agential status as the humans and then we needed to make sense of how they each participated in the composition of effects. We then needed to enter into the varied and only partially integrated everyday lives of the algorithm.

In Chapter 2, the human-shaped object and luggage-shaped object (among other objects) provided a focal point for our engagement with the algorithms' everyday lives. Computer scientists in the project sought an elegant solution that was concise (using only the minimum amount of processing power required) and could solve the problems posed by the project to the satisfaction of various audiences. Here we could get a frst glimpse of how the algorithms might engage in the everyday. Was this grasping everyday life (as if its major constituents were there prior to the work of algorithms, just waiting to be collected and displayed) or composing everyday life (a more fundamental working up from scratch of the objects to be made)? As a surprise to me, an ethnographer with an inclination towards composition, it turned out to be both. The algorithms were in the business of composing the everyday, with models built from scratch of the parameters of what it meant to be human-shaped, classifying small segments of streams of digital video data into putative humans, and then offering those forward as a means to classify the action states of those objects. Even articulating the everyday on these terms seems like a new form of composition, at least in contrast to how we might go about our everyday lives. But these algorithms also needed to grasp the everyday. They were not free to compose without limits as if there was no a priori world from which these objects could be mustered. The life of the airport, the people and objects in it had to be given a life within the algorithmic system that could be traced back through the airport and train station. Actual distances in centimetres, speeds at which people walk, distances covered, the angle and zoom of cameras among many other features of the everyday had to be accorded a form that enabled them to be grasped. And they had to be grasped in such a way by the algorithms that the journey could be made back in the other direction, from algorithm back to train station and airport. These were the demands that an elegant solution had to meet.

So our algorithms were beginning to be competent in grasping and composing everyday life. But their own lives were not without constraints. They were not just in the business of producing results, but demonstratively proving that they had produced the right kind of results. These were outputs that accountably and demonstrably accomplished the project's three ethical aims, to see less, to store less and to do so without creating any new algorithms. Elegance alone was insuffcient. To an ethnographer assessing their ethics, to an ethics board and later in ethical demonstrations, our algorithms had to continually and accountably prove their capabilities. The abandoned luggage, moving the wrong way and movement into a forbidden area algorithms had to work with other system components in Chapter 3, the User Interface, the Route Reconstruction system, probabilistic trees, algorithmic children, parameterisation, classifcation of objects and action states, to collectively demonstrate that everyday life could be improved by the emerging system. This was composition of everyday life, then, but one that was also morally improved. The world was not just grasped, but ethically enhanced. The accountable order that the algorithms could participate in, while in their experimental activities, had to intersect with a more formal sense of accountability. An opportunity had to be developed for future data subjects of algorithmic decision-making and their representatives to question the system. The algorithms also had to engage with the ethics board to begin to give effect to the ethically enhanced world. Unfortunately for our algorithms, these effects and the confdence with which they were demonstrated, began to dissipate as the system moved beyond experimentation.

In Chapter 4, it became clear that the system's ethical aims might have a value beyond experimentation, in accomplishing compliance with new regulatory demands to delete data. Deletion might provide a means to accomplish a market value for our algorithms. Yet it was here that problems began to emerge. As preparations were made to use the algorithms to distinguish between relevant and irrelevant data and provide demonstrative proof that irrelevant data could be effectively and accountably deleted, project members started to disagree. Just what should constitute adequate deletion? Changing the route by which a user connects to data, overwriting, corrupting or expunging data from the system? As the project coordinators sought the most thorough means of deletion possible, as a prior step to developing a market for the system, the computer scientists struggled to match their demands. A system log was developed to produce accountable reports for humans of the algorithms' ability to delete. But the system did no more than continually report the failures of the system: data was not deleted in its entirety, orphan frames were left behind, and the demarcation of relevant from irrelevant data came under scrutiny. The production of nothing (the deleted), required the production of something (an account of deletion), but the failure to successfully accomplish nothing (with deletion undermined by the stubborn presence of orphan frames) created a troubling something—a continually disruptive presence that questioned our algorithms' abilities to produce nothing. Much of everyday life—somewhere between 95 and 99%—it turns out is irrelevant and can be deleted. By failing to grasp all this irrelevance and instead leaving a trail of data and reports that attested to this failure, the prospects of our algorithms becoming the everyday of the airport and train station were diminished.

This was the start of some escalating troubles for the algorithms. As they continued their journey from experimentation, they had to enter into the ever greater wilds of everyday life. From experimentation in settings with matching fooring and lighting, project participants acting out the roles of normal humans, and cameras supplying data from the right angle and height and distance, at the right frame rate for our algorithms to see, our algorithms now had to grasp real space, in real time. Here people, things and events unfolded in a naturally occurring away, across different foorings and lighting conditions, at different frame rates, with humans who now acted in oddly normal ways. Children went this way and that way, adults stood still for too long, luggage did not behave as it ought and humans wore the wrong kinds of outfts that looked just like the airport foor. Grasping and composing this everyday was too challenging. Under test conditions, in place of 6 items of potentially abandoned luggage came 2654 items. The relevant and irrelevant intermingled in a disastrous display of technical ineffcacy. What had seemed like reasonable demonstrations of the algorithms' capabilities to ethical audiences, now had to be questioned. Questions of the material integrity of these demonstrations (and the extent to which a relation of undoubted correspondence could be maintained between the system put on show and the world to which it pointed) were only matched by questions of their visual integrity (of who and what was in a position to see who and what). These questions continued and even grew for a time as our algorithms moved towards their fnal demonstrations to research funders. The king of Event Detection—abandoned luggage—could only be demonstrated through a careful whittling away of confounding variables. The fooring, lighting, luggage-type, positioning, behaviour of the luggage's human owner, frame rate of the camera and other human-shaped objects of the airport each had to be closely controlled. In place of the algorithm going out into the world grasping or composing real time, real space everyday life, a more modest and controlled everyday had to be brought to the system.

And so we fnd in our fnal chapter that the algorithms are somewhat quiet. Away from the drama of contemporary academic writing and popular media stories, the algorithms take up a meek position in an Exploitation Report. In place of any fanfare regarding their technical effcacy, comes a carefully composed account, depending on imaginative and dextrous calculative work. Here, more and less valued geographical regions, customer types and inferior competitors stand in as proxies for our algorithms. The calculations, instead of talking about current technical effcacies, point towards a future potential of market value that could be achieved with investment. The performative accomplishment of the investment proposition negates the need for our algorithms' everyday life to be put on display. At the end, they are not entirely absent from our story, but from the Exploitation Report their grasp and composition of everyday life, their prospects of becoming the everyday of the airport and train station, are deleted. Goodbye algorithm.

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Simakova, E., & Neyland, D. (2008). Marketing Mobile Futures: Assembling Constituencies and Narrating Compelling Stories for an Emerging Technology. *Marketing Theory, 8*(1), 91–116.

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/ by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

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© The Editor(s) (if applicable) and The Author(s) 2019 139 D. Neyland, *The Everyday Life of an Algorithm*, https://doi.org/10.1007/978-3-030-00578-8

*Concise Oxford Dictionary*. (1999). 10th ed. Oxford: Oxford University Press.

USA. Available from: http://governingalgorithms.org/wp-content/ uploads/2013/05/3-paper-introna.pdf.

# Index

#### **A**

Accountability, 7–9, 12, 14, 26, 41, 45–53, 60–68, 74–77, 81, 87–91, 113, 133 Agency, 3, 7, 14, 47, 49, 77, 78 Algorithm, 1–13, 15–17, 22–26, 29, 31–33, 36, 39–41, 45, 46, 48–54, 57, 58, 61, 64–66, 74, 77–79, 81, 85, 88, 93, 97, 103, 105, 107, 110, 112, 113, 115, 118, 123, 132–135 as opaque/inscrutable, 3, 7, 8, 13, 22, 132 and politics, 11 and power, 3, 6–9, 14, 32, 35, 36, 49, 77 Algorithmic, 3–9, 13–16, 22–26, 28, 30, 32–41, 46, 48–53, 55–57, 60–68, 74–76, 79–81, 83–86, 89–91, 94, 95, 100, 102–104, 107, 114, 115, 118, 124–126, 128–131, 133, 134 children, 2, 6, 60, 133, 135 probability, 59 Assessment, 16, 24–26, 47, 50, 61, 94

#### **B**

Blank fgure, 80, 81, 83, 89, 90 Braudel, F., 10

#### **C**

Calculation, 1, 15, 16, 22, 78, 89, 124, 127–131, 135 Composition, 4, 11, 15, 16, 33, 118, 132, 133, 135

## **D**

De Certeau, M., 10 Deletion, 14, 15, 25, 39, 68, 74–83, 86–91, 94, 100, 124, 128, 129, 131, 134 Demonstrations, 15, 16, 22, 62, 94–97, 99–107, 113–117, 124, 133, 135 Displays of virtuosity, 95, 101, 109, 114 Dramaturgical metaphors, 94

© The Editor(s) (if applicable) and The Author(s) 2019 149 D. Neyland, *The Everyday Life of an Algorithm*, https://doi.org/10.1007/978-3-030-00578-8

#### **E**

Effects, 3, 4, 6–9, 15, 22, 32, 33, 48, 50, 132, 134 Elegance, 14, 30, 33–35, 39, 74, 133 Ethics, 24, 25, 41, 50, 61, 62, 96, 100, 101, 103, 113, 114, 133 ethics board, 26, 62, 133, 134 Everyday, 2–5, 7–16, 21–24, 32–36, 39–41, 46, 47, 49–55, 57–62, 65–68, 73, 74, 77, 78, 85, 86, 89, 90, 93, 94, 100, 105, 107, 109, 113–118, 123–125, 127, 131–135 Expectation, 4, 48, 61, 67, 93 Experimentation, 14, 22–25, 29, 31, 33, 36, 38–40, 48–50, 57, 64, 67, 74, 85, 89, 90, 100, 102, 109, 113, 124, 134

#### **F**

Frame-rates, 85

#### **G**

Garfnkel, H., 23, 51, 54, 55, 98, 119 General Data Protection Regulation, 75, 76 Goffman, E., 9, 10 Governmentality, 6, 47, 48 Grasping, 8, 68, 75, 89, 100, 102, 118, 129, 132, 133, 135

#### **H**

Human-shaped objects, 13, 14, 22, 30, 31, 34, 36–38, 40, 55, 58, 59, 63, 78, 85, 101, 115, 135

#### **I**

Integrity, 16, 87, 94, 95, 97–101, 105, 107–109, 113–119, 135 Investment, 4, 16, 124, 125, 127– 131, 135

## **J**

Judgement, 79, 86–88, 95

#### **K**

Knowing, 8, 85, 96 bodies, 10 spaces, 34, 119

## **L**

Lefebvre, H., 10

#### **M**

Markets, 4, 16, 48, 125–127 making, 16 share, 16 Mol, A., 11 Morality, 10, 13, 16, 95, 114, 116

#### **N**

Neo-Foucauldian, 47

#### **O**

Opacity, 3, 5, 26, 45, 47

#### **P**

Performativity, 16, 124–127, 130

Pollner, M., 11, 12, 40 Privacy, 25, 29, 30, 47, 62, 65, 68, 75, 76, 114, 128, 130 Probability, 59 Proof, 15, 33, 88, 129, 134

Testing, 68, 74, 84, 88–90, 94, 95, 100, 104, 110, 113 Transparency, 7, 8, 14, 41, 46, 52, 113

# **U**

Undoing, 80

#### **V**

Value, 3, 8, 15, 16, 47, 65, 68, 74, 75, 78, 80, 88, 90, 91, 96, 99, 118, 124, 126–128, 130, 131, 134, 135 Vision, 104, 107, 114, 116, 117

#### **W**

Witnessing, 2, 94, 97, 114

#### **X**

X marks the spot, 112

#### **Y**

Years of work, 67, 118

#### **Z**

Zero, 80, 81

#### **Q**

Qualculation, 78–81, 83, 84, 87, 89

#### **R**

Revelation and concealment, 95–97, 99, 101, 102, 109, 113, 114, 116 Right to be forgotten, 75, 76, 130

#### **S**

Science and Technology Studies (STS), 14–16, 22, 49, 50, 78, 94, 95, 113, 125, 127 Security, 5, 8, 13–15, 24, 29, 35, 38, 46, 53, 57, 60, 88, , 103 Something and nothing, 80, 81, 83 Success and failure, 15 Surveillance, 5, 6, 24, 25, 28, 30, 32, 34, 36, 46, 51–62, 64, 74, 83, 86, 100, 102–104, 107, 109, 110, 114, 124, 128, 130

#### **T**

Temporality, 47


*3.1. Bacteriostatic Properties*

The broad-spectrum bacterial inhibitory properties of chitosan have been confirmed by in vitro experiments and complex food antimicrobial assays [29–32]. In the process of food preservation, the use of chitosan coating or film covering the surface of food can, to a certain extent, resist microbial attack, slow down food spoilage, and thus extend the shelf life of food. For example, Wei et al. [33] studied the effects of different concentrations of chitosan on the color and surface microbial populations of frozen duck skin. The results showed that chitosan could form oxygenation competition with color-presenting substances, and 3% chitosan could effectively reduce the total number of surface colonies of duck skin during storage. In addition to the effect of chitosan concentration on the inhibition effect, molecular weight, degree of deacetylation, and bacterial species have also been shown to have an effect. Benhabiles et al. [6] studied the effect of molecular weight and degree of

deacetylation on the inhibition effect, comparing the inhibition performance of chitosan on four Gram-positive and seven Gram-negative bacteria. The results showed that chitosan with low molecular weight and a high degree of deacetylation had a better inhibition effect, and among the tested species, chitosan had an inhibition effect on all bacteria except *Salmonella typhimurium*. The pH of the solution and the presence of metal ions also have an effect on the bacterial inhibitory effect of chitosan. Jing et al. [34] used ANOVA to test the data and concluded that the inhibitory activity of chitosan increased with decreasing pH in the range of >4 and <6. Comparing the effects of metal ions on the inhibitory activity of chitosan at a pH of 4, the results showed that Ca2+ and Mg2+ significantly reduced the inhibitory ability against Gram-negative bacteria. It can be seen that the inhibitory activity of chitosan is related to a variety of factors, with interlocking effects among them, and the best inhibition results can be obtained only by comprehensively analyzing all of the factors.

Although the information on the antimicrobial activity of chitosan is controversial, it is generally accepted that yeasts and mycobacteria are the most sensitive organisms to chitosan, followed by Gram-positive and Gram-negative bacteria [35]. The antifungal activity of chitosan is mainly attributed to the inhibition of mycelial growth and spore germination [36]. Early studies showed that chitosan inhibits spore germination, budding tube elongation, and radial growth by sequestering metals, minerals, trace elements, or nutrients required for fungal growth [37]. The inhibition of pathogenic fungi increases with increased chitosan concentration [38], but its application is hindered by low solubility at physiological pH due to its low charge density. Therefore, increasing the positive charge density of chitosan may be the most effective way to improve its solubility and antifungal activity [39]. The antifungal activity of chitosan also depends on its molecular weight [40], which affects the morphogenesis of the fungal cell wall [41]. It has also been shown that chitosan's inhibition of pathogenic fungi in fruits may be related to its ability to induce increased production of, e.g., polyphenol oxidase and peroxidase [42].

As research progressed, hypotheses on the mechanism of chitosan inhibition were put forward, but the exact mechanism of inhibition has still not been determined. Feng et al. [43] measured the OD of cell contents under UV light at a wavelength of 260 nm and showed an increase in OD. Using transmission electron microscopy to observe changes in the ultrastructure of *E. coli* and *S. aureus* before and after the action of chitosan, it was observed that the cell membrane was broken, accompanied by a large amount of content spillage (DNA and mRNA) at the periphery and bacterial apoptosis. Similarly, Hui et al. [44] evaluated the antibacterial activity of chitosan acetate solution against *E. coli* and *S. aureus*. The morphology of bacteria after chitosan treatment was observed by transmission electron microscopy. The integrity of the cell membranes of both bacteria and the permeability of the inner and outer membranes of *E. coli* were investigated by measuring the absorption values at 260 nm UV, fluorescence changes in cells treated with the fluorescent probe 1-N-phenylnaphthylamine, and the release of cytoplasmic h-galactosidase activity. The results showed that chitosan ultimately damaged the bacteria by increasing the permeability of the inner and outer cell membranes, releasing the cell contents. This damage is most likely caused by electrostatic interactions between the NH3+ group in the chitosan acetate solution and the phosphoryl group of the phospholipid fraction of the cell membrane. Imelda et al. [29] confirmed the disruptive effect of chitosan on protein synthesis by β-galactosidase expression assay.

The above studies showed that chitosan can inhibit bacteria by disrupting bacterial cell membranes and protein synthesis. Ming et al. [45] pointed out that chitosan has high chelating ability for various metal ions (including Ni2+, Zn2+, Co2+, Fe2+, Mg2+, and Cu2+) under acidic conditions. As a chelating agent, chitosan selectively chelates metal ions, thus playing a key role in microbial growth by inhibiting the growth and reproduction of microorganisms. Gooday et al. [46] pointed out that when chitosan crosses the cell wall of fungal pathogens with plant hydrolases as host, it penetrates the nucleus of the fungus, and the positively charged chitosan interacts with the negatively charged DNA, affecting RNA transcription and protein synthesis, thus achieving fungal inhibition.

In summary, there are three generally accepted theories about the mechanism of chitosan inhibition, the first of which is inter-charge interactions. As shown in Figure 3 [17], the electrostatic interaction between R-NH3+ of chitosan and negatively charged groups on the bacterial surface destabilizes the structure of the cell membrane, and the leakage of intracellular material leads to microbial death. The second theory concerns the chelating property of chitosan with metal ions. Chitosan chelates metal ions on the surface of bacteria, forming a microbially unavailable chelate, thus inhibiting microbial growth. The third theory holds that chitosan enters the cell and binds to DNA, and affects RNA transcription and protein expression, thus achieving the effect of bacterial inhibition. Although the above views are generally accepted, there are still many scholars who believe that the molecular weight and degree of deacetylation, and the differences between bacterial species, should also be considered when investigating the inhibition mechanism of chitosan.

**Figure 3.** Mechanisms used to explain antibacterial activity of chitosan.


**5.4. Flour, bread, and biscuits**

One year after Chernobyl, corn, grains, bread, shortbreads, and pasta were seriously conta‐ minated with radiocaesium. In 1987, cereals from 1986 were taken into production. Seven of 17 breads and shortbreads and one pasta were over the limit value of 1250 Bq/kg and had to be withdrawn. Twenty-one more samples were over the tolerance level of 100 Bq/kg. As a consequence, the control of flour and flour products was intensified over the next years. However, with the exception of one sample in 1988, no more violations of the tolerance value were noticed. In 2011, the radiation level fell down to approximately 0.5 Bq/kg radiocaesium in cereals.

Natural radionuclides are present in cereals (radium 1–5 Bq/kg and thorium 0.1–1 Bq/kg). The levels of radium are near the limit value of 5 Bq/kg.

### **5.5. Meat**

Our own γ-analyses of meat in 1986 showed values in two categories: for cows and calves, the level was low (57±71 Bq/kg), whereas meat from sheep and lamb was quite higher contami‐ nated (645±744 Bq/kg radiocaesium). In the following year, the radiocaesium levels were elevated to the same level: sheep and lamb 550±198 Bq/kg and cow and calf 417±384 Bq/kg. During 1986, the Federal Food Safety and Veterinary Office (former Federal Office for Veterinary Affairs) analysed more than 1700 meat samples of sheep, goat, cow, pig, and game mainly from eastern and southern Switzerland. The contamination levels in the meat were higher in regions with higher depositions of fallout. In southern Switzerland, the radiocaesium activity concentrations varied from 40 to 4400 Bq/kg. The highest value was found in a goat. In contrast to this, in eastern Switzerland, values were from 40 to 1300 Bq/kg. In the other parts of Switzerland, even lower values were found. Almost 2000 samples from imported meat were analysed. Here, 251 samples had to be rejected due to values more than 600 Bq/kg. During the year 1986, a reduction of the contamination level was observed, with the exception of southern Switzerland (cantons of Ticino and Grison). The biological half-life for radiocaesium was calculated to be approximately 50 days for sheep and goat and approximately 30 days for game. Pigs showed a half-life of 90 days [49].

#### **5.6. Game**

Game became of interest when high radioactive contaminations of reindeer were reported in Norway and Sweden. The northern European countries were more highly contaminated with radioactive fallout from Chernobyl than other European countries. Wild berries, mushrooms, and lichens are the main food of reindeers. These were seriously contaminated with this fallout. Thousands of animals had to be burnt because of a violation of radiocaesium values that were too high. The survey of game in Switzerland began in autumn 1986. We analysed meat from five roe deer and deer with γ-ray spectrometry. The activities were not high. Nevertheless, in 1987, the contamination of the game meat showed higher values up to 7 kBq/kg radiocaesium. From 1986 on, game was investigated yearly. In 1990, four objections had to be executed according to our measurements. It seemed that chamois were the most contaminated game in Switzerland. After 1995, the monitoring programme was reduced. The detected radioactivity was under the tolerance limit of 600 Bq/kg.

Wild boars are an exception. The southern parts of Switzerland, such as in Bavaria (Germany), are more contaminated landscapes. Here, the contamination of wild boar remains a problem up to today. Wild boars search for their food on the ground. Elaphomyces, a truffle species, grow underground and are able to enrich radiocaesium from soil. It is estimated that these fungi can be up to 20% to 30% of the food of wild boars. In 2012, in southern Bavaria and the Bavarian Wood, the contamination levels were up to 9.8 kBq/kg wild boar and up to 430 Bq/kg in roe deer [50]. In Switzerland, the State of Ticino, in collaboration with the Federal Office of Public Health, investigated wild boars in 2013 and 2014. In 2013, 28 wild boars of a total of 470 animals violated the limit value of 1250 Bq/kg. In 2014, they found 13 such contaminated animals. These animals had to be confiscated [17]. In contrast, in the State of Zurich, the State Laboratory Zurich found no violations when they analysed 80 wild boars. The mean activity was low with 28 Bq/kg radiocaesium [51]. In 2014, the Umweltinstitut München reported from their monitoring programmes for wild-grown vegetables and game. More than 2000 samples showed contamination levels over the limit value of 600 Bq/kg, and 141 samples showed radiocaesium activities of more than 10,000 Bq/kg. Ten samples contained more than 16,000 Bq/kg. The maximum value was 27,800 Bq/kg [52]. Therefore, the serious contamination rests a problem in Bavaria.

Wild boars have to be surveyed over the coming years. Fortunately, they are not a widely consumed game. More important are deer and roe deer, which show considerably lower contaminations [53, 54].