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Ep 2. A.I. in the Driver’s Seat



Listen to the podcast and watch this video and read the file attached and then start working
Please respond to the following questions:
1. Strpias argues that use of algorithms has allowed humans to offloadcertain aspects of culture onto computational processes. This is resulting in what he callsprivatizationof the processes that shape cultural values. What does he mean by this? Do you agree?
2. Faridexamines the use of predictive algorithms in criminal sentencing to eliminate problems with racial bias in court. Howdid he study the problem and what were his findings? Does he find algorithms to be auseful tool in this context? Why or why not?
3. This episode of AINationexplores the emerging technology of self-driving cars. Podcast host Malcolm Burnley argues that “AI is kind of like an alien intelligence. The way it handles information is hugely different from the way we do. There are some ways in which it’s superior to us, and other times it utterly fails at things we’d think of as common sense.” How might that impact the way it drives a car? Cite specific examples from the podcast. What are your own thoughts about self-driving cars? (i.e. Are you looking forward to a day when these are the norm or are you more wary of this form of transportation?)
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European Journal of Cultural Studies
2015, Vol. 18(4-5) 395 –412
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DOI: 10.1177/1367549415577392
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e u r o p e a nj o u r n a lo f
Algorithmic culture
Ted Striphas
Indiana University, USA
Abstract
Over the last 30 years or so, human beings have been delegating the work of culture
– the sorting, classifying and hierarchizing of people, places, objects and ideas –
increasingly to computational processes. Such a shift significantly alters how the
category culture has long been practiced, experienced and understood, giving rise to
what, following Alexander Galloway, I am calling ‘algorithmic culture’. The purpose
of this essay is to trace some of the conceptual conditions out of which algorithmic
culture has emerged and, in doing so, to offer a preliminary treatment on what it is.
In the vein of Raymond Williams’ Keywords, I single out three terms whose bearing
on the meaning of the word culture seems to have been unusually strong during the
period in question: information, crowd and algorithm. My claim is that the offloading
of cultural work onto computers, databases and other types of digital technologies
has prompted a reshuffling of some of the words most closely associated with
culture, giving rise to new senses of the term that may be experientially available
but have yet to be well named, documented or recorded. This essay, though largely
historical, concludes by connecting the dots critically to the present day. What is at
stake in algorithmic culture is the gradual abandonment of culture’s publicness and
the emergence of a strange new breed of elite culture purporting to be its opposite.
Keywords
Algorism, algorithm, algorithmic culture, big data, crowd, culture, information,
keywords, Raymond Williams
Easter 2009 might well be remembered for Amazon.com’s having outshined Jesus Christ.
That much was true on Twitter, at any rate, where, during that long weekend in April, a
sudden influx of short missives about the online retailer propelled it to the Number 1 spot
Corresponding author:
Ted Striphas, Department of Communication and Culture, Indiana University, Classroom
Office Building, 800 E. 3rd Street, Indiana University, Bloomington IN 47405, USA.
Email: striphas@indiana.edu; Twitter: @striphas
577392ECS0010.1177/1367549415577392European Journal of Cultural StudiesStriphas
research-article2015
Article
mailto:striphas@indiana.edu
Twitter: @striphas
http://crossmark.crossref.org/dialog/?doi=10.1177%2F1367549415577392&domain=pdf&date_stamp=2015-06-16
396 European Journal of Cultural Studies 18(4-5)
on Twitter’s trending topics list, unseating the Prince of Peace along the way (James,
2009b). As the Beatles learned back in 1966, however, ‘more popular than Jesus’ (as
John Lennon had claimed of the band) is not necessarily an enviable position in which to
find oneself. The hashtag to which the Twitterati directed tens of thousands of messages
– #AmazonFail – indicated that something had gone terribly wrong with company. Why,
they wondered, had Amazon apparently begun excluding gay and lesbian–themed books
from its sales rankings, searches and bestseller lists?
Author Mark R Probst first brought the issue to widespread attention when, on Good
Friday, he noticed that several gay romance books had lost their Amazon sales rankings,
including his own novel, The Filly. Hoping the matter was a simple mistake, he wrote to
Amazon customer service. The agent who emailed Probst explained that Amazon had a
policy of filtering ‘adult’ material out of most product listings. Incensed, Probst (2009)
posted an account of the incident on his blog in the wee hours of Easter Sunday morning,
pointing out inconsistencies in the retailer’s policy. The story was subsequently picked
up by major news outlets, who traced incidences of gay and lesbian titles disappearing
from Amazon’s main product list back to February 2009 (Lavallee, 2009; see also Kellog,
2009; Rich, 2009).
In a press release issued on Monday afternoon, a spokesperson for Amazon attributed
the fiasco to ‘an embarrassing and ham-fisted cataloging error’. More than 57,000 books
had been affected in all, including not only those with gay and lesbian themes but also
titles appearing under the headings ‘Health, Mind, Body, Reproductive and Sexual
Medicine, and Erotica’ (quoted in James, 2009a; see also Rich, 2009). An Amazon techni-
cian working in France reportedly altered the value of a single database attribute – ‘adult’
– from false to true. The change then spread globally throughout the retailer’s network of
online product catalogs, de-listing any books that had been tagged with the corresponding
metadata (James, 2009b). This was not homophobia, Amazon insisted, but a slip-up
resulting from human error amplified by the affordances of a technical system.
In the wake of the controversy, author and lesbian, gay, bisexual and transgender
(LGBT) activist Larry Kramer observed: ‘We have to now keep a more diligent eye on
Amazon and how they handle the world’s cultural heritage’ (quoted in Rich, 2009).
Indeed, Amazon may have started as a retailer, but it has grown into an exemplar of the
many ways human beings have been delegating the work of culture – the sorting, clas-
sifying and hierarchizing of people, places, objects and ideas – to data-intensive compu-
tational processes.1 Amazon’s back-end data infrastructure is so vast, in fact, that in 2006
it began selling excess capacity to clients under the name Amazon Web Services. It also
collects sensitive data about how people read through its Kindle e-book devices – which
is to say nothing of how it profiles and then markets products to customers based on their
browsing and purchasing patterns (Striphas, 2010). What one sees in Amazon, and in its
kin Google, Facebook, Twitter, Netflix and many others, is the enfolding of human
thought, conduct, organization and expression into the logic of big data and large-scale
computation, a move that alters how the category culture has long been practiced, expe-
rienced and understood. This is the phenomenon I am calling, following Alexander R
Galloway (2006), ‘algorithmic culture’.2
The purpose of this essay is to trace one set of conditions out of which a data-driven
algorithmic culture has developed and, in doing so, to offer a preliminary sense of what
Striphas 397
‘it’ is. The overarching impulse here is historico-definitional, though there are many ways
to execute such a project. One could focus on the propagation of ‘truthful’ statements (i.e.
discourses) pertaining to algorithmic culture (Foucault, 1972), or map the sociological
circuitry through which the concept has made its way through the world (Mannheim,
1955). Or instead, one could take an etymological tack in attempting to trace the origins
of particular words, or adopt a philological thrust in trying to apprehend definitive usages
of words in history.
While this essay combines elements of these approaches, it is inspired primarily by
Raymond Williams’ (1983) work on keywords. This piece emphasizes moments of cat-
achresis – instances of lexical ‘misuse’ that help concretize an alternative semantics for
particular words and word clusters. These moments enable new or at least different ways
of figuring reality through language, for example, in drawing what was long taken to be
the conceptual sine qua non of qualitative human experience – culture – into the orbit of
computational data processing (see, e.g. Kittler, 2006). It is a contention of this essay that
the semantic dimensions of algorithmic culture (and also then of the related phenomena
of big data, data mining and analytics, the themes of this special issue of European
Journal of Cultural Studies) are at least as important as the technological ones, the latter,
for perhaps obvious reasons, tending to command the spotlight. But as Williams (1983)
noted, ‘some important social and historical processes occur within language’, giving
rise to new existential territories that only later come to be populated by technical arti-
facts (p. 22; see also Striphas, 2014).
Moreover, a keywords approach is useful in apprehending latencies of sense and
meaning that persist, insist and subsist in contemporary usage as ‘traces without … an
inventory’ (Gramsci, 1971: 324; see also Seigworth, 2000: 237). Logging that inventory,
as it were, allows one to not only situate algorithmic culture within a longer durée but
also reflect on claims to objectivity and egalitarianism that are now made in its name.
Beyond semantics, what is at stake in algorithmic culture is the gradual abandonment of
culture’s publicness and thus the emergence of a new breed of elite culture purporting to
be its opposite.
Keywords today
Gary Hall (2002) opens the final section of Culture in Bits with the line, ‘what if Richard
Hoggart had had email?’ (p. 126). This is tantamount to asking, ‘what would the work of
cultural studies’ canonical figures look like were it composed today, a time of ubiquitous
digital computational technologies?’ Imagine, say, Raymond Williams (1958) were writ-
ing Culture and Society having to confront the #AmazonFail episode. How might he
make sense of the entwining of culture, which he posited as a ‘court of human appeal’
(Williams, 1958: viii), and computational decision-making (see also Hallinan and
Striphas, 2014)?
Williams’ (1983) original project was to show how culture, once a relatively obscure
word in English-language usage, became ‘one of the two or three most complicated words’
by the start of the 20th century (p. 87). He did so by tracing semantic shifts across a net-
work of terms, many of which formed the basis for his compendium, Keywords (Williams,
1976, 1983). The introduction to Culture and Society offers a more succinct version of the
398 European Journal of Cultural Studies 18(4-5)
story, focusing on five words whose history and interconnection uniquely embodied ‘a
general change in our characteristic ways of thinking about our common life’: industry,
democracy, class, art and culture (Williams, 1958: xiii). With the first four, Williams estab-
lished a set of semantic coordinates, which he then used to chart culture’s shifting meaning
and importance: from a pre-modern understanding grounded in husbandry to a more capa-
cious, modern view – ‘a thing in itself’, encompassing not only ‘the general body of the
arts’ but also ‘a whole way of life, material, intellectual, and spiritual’ (p. xvi).
Spanning the years 1780–1950, Culture and Society is bookended by two major his-
torical events, namely, the industrial revolution and the end of the Second World War.
The latter helped precipitate another great transformation referred to variously as the
computer revolution, the communications revolution, the cybernetics revolution and so
on (Beniger, 1986: 4–5). Prescient as he was, it is doubtful Williams grasped the full
significance of his endpoint. More likely, he chose 1950 because the date marked mid-
century, the moment in which the symbolics of history and futurity mingle more or less
freely. Still, one can see Williams (1958) grasping to understand new technological con-
texts in his reflections on communication appearing in the conclusion to Culture and
Society (pp. 296, 300–304, 313–319). It was not until the publication of The Sociology of
Culture, however, that Williams (1981) broached the relationship between culture, infor-
mation and digital technologies – but then only in passing, in the work’s conclusion (pp.
231–232).3 He may not have been able to work out a fully revised theory of culture per
se, but he managed to lay important groundwork for assessing how the semantic – and
hence practical and experiential – coordinates of culture had shifted since 1950.
We are still living in the midst of this shift, although the nascent trends and tendencies
Williams tried to make sense of in the early 1980s are more coherent today. The
#AmazonFail episode illustrates this point, underscoring the degree to which shopping,
merchandizing and a host of other everyday cultural activities are now data-driven activ-
ities subject to machine-based information processing (Striphas, 2009: 81–110). Indeed,
the incident would not have been intelligible, much less possible, without a reshuffling
of the terms surrounding the word, culture. Those that Williams (1958) identified in
Culture and Society remain important, to be sure, but in recent decades a host of others
have emerged. An extended list might include analog, application, cloud, code, control,
convergence, copy, data, design, digital, format, free, friend, game, graph, hack, human,
identity, machine, message, mobile, network, noise, peer, platform, protocol, search,
security, server, share, social, status, web and many more.4 Like Williams, however, I
want to single out a small group of terms whose semantic twists and turns tell us some-
thing about senses and meanings of the word culture that are available today, and also
then about the politics of big data, data mining and analytics. Williams identified the first
one – information; the other two – crowd and algorithm – are my own.5
Information
If culture’s usage is unusually ‘complicated’, then information’s is equally contradictory.
John Durham Peters (1988) describes its etymology as ‘a history full of inversions and
compromises’ (p. 10). Like a moody teenager, it swings from specificity to generality,
and from the empirical to the abstract. Yet, this range is also what makes information
Striphas 399
intriguing from the standpoint of algorithmic culture, which channels an older sense of
the word that the Oxford English Dictionary or OED describes as, ‘now rare’
(‘Information’, n., n.d.; see also Peters, 1988: 11; Gleick, 2011).
When information enters the English language sometime around the 12th or 13th
century CE, chiefly from Latin, the tension at the heart of the word is already becoming
manifest. At this early stage, it operates in two main semantic registers: religion and law.
The use that the OED claims is ‘now rare’ is the religious one, although it might be more
apt to describe it as spiritual, even deific. Here, information denotes ‘the giving of form
or essential character to something; the act of imbuing with a particular quality; anima-
tion’ (‘Information’, n., n.d.). This definition posits an irreducible connection between
the shaping of something and the endowment with character, substance or life.
Information’s juridical definition derives from the codes of ancient Roman legal pro-
cedure. Broadly, it refers to ‘the imparting of incriminating knowledge’, and more specifi-
cally, in US law, to ‘an accusation or criminal charge brought before a judge without a
grand jury indictment’ (‘Information’, n., n.d.). Although information here depends on
and passes among incarnate human agents, this definition differs from the more recent
understanding, ‘knowledge communicated concerning some particular fact, subject, or
event’ (‘Information’, n., n.d.). In the legal sense, one does not refer to information per se
but to something narrower in scope – ‘an information’, or even ‘information’. The corre-
sponding verb form, ‘laying of information’, is a special type of communication – a
speech act – whose outcome is to transform the innocent into the accused and to set forth
social rituals intended to restore order in the wake of some disturbance. Here the defini-
tion comes closest to the religious sense of character- or quality-giving, although now the
information ‘source’ is secular interaction.
The influence of early modern empiricism and idealism on the word information must
not be underestimated. The definition to which I referred in passing, ‘knowledge com-
municated concerning some particular fact, subject, or event’, is indicative of the term’s
encounter with these crosscurrents of early modern thought, for it posits information not
as intrinsic quality or character but as extrinsic sense data. This bit of semantic drift is
significant, underscoring how far the locus of information has shifted from pre-modern
through early modern times and beyond. Although it continued to refer to a kind of exis-
tential work, divine or worldly, gradually, a more object-oriented definition sidelined this
sense of the word.
The passive voice construction ‘knowledge communicated’ is important to dwell on,
moreover, because it indicates that information – now conceived as a thing – always
emanates from some source external to one’s self. In the framework of Immanuel Kant,
it belongs to the noumenon, or the realm of unmediated sense data. This marks a signifi-
cant departure from the spiritual and legal definitions, both of which locate information
in the body vis-a-vis its incarnations, godly or performative. The object-oriented defini-
tion, on the other hand, inaugurates a process of abstracting information from the body;
instead of being vested there, information becomes a separate raw material that must be
given order vis-a-vis our cognitive faculties (‘Information’, n., n.d.).
The 20th century information theorist Norbert Wiener famously quipped that the nat-
ural world consists of ‘a myriad of To Whom It May Concern messages’ (quoted in
Rheingold, 1985: 113), drawing a line back to the work of his early-modern forebears.
400 European Journal of Cultural Studies 18(4-5)
They similarly imagined a world bombarding us with sensory input. This was a broken,
not a direct line, however, resulting in an even more diffuse meaning for the word. If
information were akin to a ‘To Whom It May Concern Message’, then it need not be
directed to anyone in particular. More to the point, in Wiener’s formulation, it need not
be directed to anyone at all.
Apropos, the stars of Wiener’s two major books on cybernetics and information are
neither the brain nor the cognitive structures that purportedly allow people to make our
way in the world. They are, instead, photoelectric cells and antiaircraft guns, and more
utilitarian things like automatic door openers and thermostats (Wiener, 1954, 1961). In
contrast to wind-up clocks and other simple mechanical devices, which function in a
manner more or less unattuned to environmental conditions, these machines ‘must be en
rapport with the world by sense organs’ and adjust their behavior according to the infor-
mation they receive (Wiener, 1954: 33; see also pp. 21–22). In 1944, the physicist Erwin
Schrödinger (1967 [1944]) argued that life ‘feeds on negative entropy’, meaning that life
is nothing more and nothing less than a small pocket of order within a world abuzz with
information (p. 70). Four years later, Wiener told a similar story but threw in a major plot
twist. If machines possessed an appetite for information, then apparently information
was not particular to human beings.
From the Second World War on, then, machines begin being seen not merely as useful
things but as custodians of orderliness. Critical to their work was information, which
Gregory Bateson (2000 [1971]) defined as ‘a difference which makes a difference’ (p.
315). Bateson, like Wiener, identified as a cyberneticist, so in one sense it should not
surprise to find him defining information in terms of bits, or simple yes–no decisions.
But in another sense, his definition may surprise. Bateson was a trained anthropologist
and spouse of Margaret Mead, to whom he was married for 14 years. They had one child
together, Mary Catherine, who also became a noted anthropologist. In a family so thick
with interest in people and culture, it is telling that Bateson never bothered with the ques-
tion ‘to whom?’ when he called information ‘a difference which makes a difference’. By
the early 1970s, information was only residually the process by which people and things
were endowed with substance, trait or character – in-formed, as it were. It had become,
instead, a counter-anthropological leveler, smoothing over longstanding differences
between humans and machines: Inform-uniform. James Gleick (2011) puts the matter
succinctly: ‘it’s all one problem’ (p. 280).
In 1966, Michel Foucault concluded The Order of Things (1971 [1970]) by claiming
that ‘man is an invention of recent date … [a]nd one perhaps nearing its end’ (p. 387).
Six years later, Gilles Deleuze and Félix Guattari (1983) opened Anti-Oedipus by pro-
claiming that ‘everything is a machine’ – plant life, animal life, mechanical devices,
electronic goods, economic activities, celestial bodies and more (p. 2). Sandwiched
between them was Bateson, the an-anthropic anthropologist for whom cultural life
becomes one type of information processing task among many. One can also see emerg-
ing the sense of cultural objects, practices and preferences as comprising a corpus of data
(from the Latin, ‘something given’), albeit data that exceed the traditional view of the
human sciences in the agnosticism toward the intended recipient. No longer would
human beings hold exclusive rights as cultural producers, arbiters, curators or interpret-
ers – a welcome development, perhaps, given the shame, disrespect and brutality elites
Striphas 401
have long exacted in the name of cultural difference. But what if the apparent uniformity
between people and machines resulted in cultural practices and decision-making that
were no better in-formed?
Crowd
The etymology of the word crowd is, like that of information, a study in polarity reversal.
It entered the English language around the 15th century CE as an adaptation of verbs
extant in Dutch, German and Frisian denoting pressuring or pushing. The English verb
form ‘to crowd’ preserves this early meaning of the word, although in some contexts the
element of physical force may be figurative rather than literal. The OED mentions that
crowd was ‘comparatively rare down to 1600’, which means its rise roughly coincides
with early modernity (‘Crowd’, n.d.). The noun form of the word has often been used
interchangeably with mass, mob, multitude and throng to refer to large gatherings of
people, generally in public, especially in urban settings. Frequently, it denotes imped-
ance, inefficiency and frustration, as in the expressions ‘fighting the crowds’ and ‘three’s
a crowd’. It also conveys individual anonymity and engaged inaction, as in the phrase, ‘a
crowd of onlookers’.6 For these reasons crowd has, until recently, harbored almost exclu-
sively pejorative connotations.
Semantically, crowd comes fully into its own in the 19th century, becoming a mainstay
of journalistic and scholarly attention.7 Charles Mackay’s Extraordinary Popular Delusions
and the Madness of Crowds, first published in Britain in 1841, is a key text in this regard.
The book chronicles incidents in which, as Mackay (2001 [1841]) puts it, ‘whole commu-
nities suddenly fix their minds upon one object, and go mad in pursuit of it’ (p. ix). The list
ranges from stock bubbles to hairstyles, catch phrases, slow poisoning, dueling, occult
practices and the mania for tulips in 17th century Holland. It is a capacious book, yet one
that offers surprising little in the way of explicit insight into crowds – at least beyond their
guilt by association with practices and events that, for Mackay, evidenced the collective
abandonment of reason. Instead, he seems to play to the conventional wisdom of the time:
‘Men, it has been well said, think in herds; it will be seen that they go mad in herds, while
they only recover their senses slowly, and one by one’ (Mackay, 2001 [1841]: x).
But Mackay does not only play to the conventional wisdom – he also plays upon it.
Preceding his statement about ‘thinking in herds’ is a passing reference to an analogous
concept: the ‘popular mind’ (Mackay, 2001 [1841]: x). Terminologically, it is a small
difference, but semantically it is a bait-and-switch. The verb phrase ‘thinking in herds’
would seem to designate an active, living process, albeit one in which any individual
contribution registers diffusely. The noun form ‘popular mind’ largely elides that pro-
cess, positing some overarching thing referring to everyone in general and no one in
particular. And in this way, crowd’s etymology closely parallels that of information,
which follows the term’s divestiture from the human body, its transformation into an
immaterial object and its dispersal into the world.
This way of conceiving of crowds culminates in Gustave Le Bon’s (2002 [1895]) The
Crowd: A Study of the Popular Mind. Le Bon offers something like the explanatory
framework absent in Mackay. Le Bon’s book is occasioned by ‘the entry of the popular
classes into political life’, whom he paints a vicious, unthinking horde: ‘History tells us
402 European Journal of Cultural Studies 18(4-5)
that from the moment when the moral forces on which a civilisation rested have lost their
strength, its final dissolution is brought about by those unconscious and brutal crowds
known, justifiably enough, as barbarians’ (Le Bon, 2002 [1895]: xii–xiii; see also Arnold,
1993 [1869]).
Le Bon’s book has been read, understandably, as an attack on crowds (see, for exam-
ple, Milgram and Toch, 2010; Surowiecki, 2004). It is one, to be sure, and an elegy for
the decline of privileged minority rule akin to Edmund Burke’s (1999 [1790]) Reflections
on the Revolution in France. Yet, there is a tone of resignation evident in Le Bon’s prose,
suggesting a kind of begrudging acceptance of the emerging political realities of the
time: ‘The age we are about to enter will in truth be the era of crowds’, he states (Le
Bon, 2002 [1895]: x; emphasis in original). This may help to explain why The Crowd
also contains a handful of passages in which Le Bon offers a more equivocal view, such
as this one: ‘What, for instance, can be more complicated, more logical, more marvelous
than a language? Yet whence can this admirably organised production have arisen, except
it be the outcome of the unconscious genius of crowds’ (Le Bon, 2002 [1895]: v)?
Whether by default or by design, Le Bon was drawing on a subterranean line of think-
ing about crowds. This line developed in the overlap of Classical Liberalism and the
Scottish Enlightenment and received its most enduring expression in the work of Adam
Smith. It was Smith (1977 [1776]) who, in An Inquiry into the Causes of the Wealth of
Nations, struggled to make sense of apparently spontaneous economic activities whose
outcome was – in Le Bon’s words – ‘admirably organised production’. Yet, the figure of
the crowd is noticeably absent from Smith. In fact, the word crowd appears only four
times in his 375,000 word magnum opus, and only then in verb form. His figure is a dif-
ferent one, and of a different kind, although it performs rhetorical work comparable to Le
Bon’s ‘genius’ crowd. This is the famous ‘invisible hand’, which, in Smith’s (1977
[1776]) view, aligns the interests of individual economic actors with the needs of a soci-
ety as a whole (p. 477).
Mysterious, ghostlike, the ‘invisible hand’ is essentially a deus ex machina of eco-
nomic activity, and in this regard it is not too far removed from the spiritual sense of
information mentioned earlier. In the 20th century, Friedrich A Hayek would make the
link more explicit, helping to bolster the more affirmative view of crowds nascent in both
Smith and Le Bon. The key work here is Hayek’s (2007 [1944]) Road to Serfdom, pub-
lished in 1944, arguably the strong state’s high-water mark in both Europe and the United
States. Hayek believed there ought to be some force to which was assigned the task of
holding the state in check; for him, that force was the economic sphere. Hence, his desire
to strip the state of the responsibility of economic planning and to leave the task of coor-
dinating economic activities up to individual actors dispersed far and wide (Hayek, 2007
[1944]: 232). Instead of positing that coordination resulted from the arcane workings of
an invisible hand, Hayek stressed the crucial role that information – his word – played in
choreographing this intricate group dance, particularly through the price system (Hayek,
2007 [1944]: 95).
Like Smith, Hayek had little to say about crowds per se. His understanding of the indi-
vidual, however, harkened back to the earliest English-language sense of crowd as the
exertion of force on others. And with this, he helped to usher the idea of the intelligent,
constructive crowd more fully into view. He was not alone in this endeavor. In 1965, the
Striphas 403
economist Mancur Olson (1971), a friend of Hayek, refuted the claim that groups were
intrinsically stupid and irrational by describing the hidden ‘logic’ underlying collective
action.8 So, too, with sociologist Stanley Milgram, whose early work on obedience to
authority was given subtlety and dimension in his later work on crowds, where he disman-
tled the view that crowds caused otherwise mindful people to become deluded (Milgram,
2010; Milgram and Toch, 2010). Finally, inasmuch as he was Hayek’s ideological oppo-
site, we must nonetheless reckon with the contributions Raymond Williams made to the
redemption of crowds. The conclusion to Culture and Society is an extended critique of
the notion of masses-as-mob, culminating in the insight, ‘there are in fact no masses; there
are only ways of seeing people as masses’ (Williams, 1958: 300). The alternatives
Williams proposes – ‘community’ or ‘common culture’ – bear an uncanny resemblance to
the sense of crowd I have been tracing here: a ‘complex organization, requiring continual
adjustment and redrawing’; denying the individual the possibility of ‘full participation’,
while still granting her or him a modicum of effect or influence; and incapable of being
‘fully conscious of itself’ (Williams, 1958: 333–334).
It is this set of positive connotations that crystallizes into contemporary terms like
‘crowdsourcing’, ‘crowd wisdom’ and a host of cognates, all of which have entered
popular usage over the last two decades or so: ‘hive mind’, ‘collective intelligence’,
‘smart mobs’, ‘group genius’ and more (see, for example, Howe, 2008; Jenkins, 2006;
Kelly, 1995; Levy, 1999; Rheingold, 2002; Sawyer, 2007; Shirky, 2008; Surowiecki,
2004; Tapscott and Williams, 2006). The translation between then and now is hardly
perfect, owing to the diverse traditions out of which this vision of crowds has emerged,
which is to say nothing of the technological transformations that have occurred over the
last century. Indeed, when Williams (1958) wrote about the ‘solidarity’ necessary to sus-
tain a ‘common culture’ (pp. 332–338), could he have anticipated the degree to which,
today, that solidarity would be forged computationally? And what then to make of the
redemption of crowds, when proprietary computer platforms have become the major
hubs for interaction online?9
Algorithm
Compared to information and crowd, algorithm is a less obvious keyword by means of
which to make sense of culture today. If the former two terms could be considered domi-
nant, or prevalent, as judged by their popular usage, then the latter would best be
described as emergent, or restricted, though tending in the direction of conventionality.
Yet, as James Gleick (2011) puts it in The Information, ‘[t]he twentieth century gave
algorithms a central role’ (p. 206).10
Algorithm comes to modern English from Arabic by way of Greek, medieval Latin,
Old French and Middle English. Historically, it has maintained close ties to the Greek
word for number, arithmós (αριθμός), from which the English form arithmetic is derived.
Algorithm’s most common contemporary meaning – a formal process or set of step-by-
step procedures, often expressed mathematically – flows from this connection, although
the OED insists it is a point of etymological ‘perversion’ (‘Algorism’, n., n.d.). In fact,
the word is a ‘mangled transliteration’ of the surname of a 9th century mathematician,
Abū Jafar Muḥammad ibn Mūsā al-Khwārizmī, who lived much of his life in Persia
404 European Journal of Cultural Studies 18(4-5)
(‘Algorithm’, n., n.d.). Algorithm is recorded in English for the first time at the beginning
of the 13th century CE as augrim, in Chaucer’s Canterbury Tales, whereupon it under-
goes a long series of orthographic transformations before settling into what, from the
early 18th century until the early 20th century, becomes its conventional spelling, algo-
rism (Karpinski, 1914: 708). The present-day rendering of the word, algorithm, likewise
appears around the start of the 18th century, but it does not become the standard orthog-
raphy until almost 1940.
The confusion stems mainly from two key mathematics texts attributed to al-Khwārizmī
from which his name, and eventually two different though related senses of the word
algorithm wind their way into English. The first manuscript, Al-Kitāb al-Mukhtaṣar fī
ḥisāb al-jabr wa-al-Muqābala (The Compendious Book of Calculation by Restoration
and Balancing), introduced many of the fundamental methods and operations of algebra.
It is the primary work through which the word algebra itself, adapted from the Arabic
al-jabr, diffused through Moorish Spain into the languages of Western Europe (Crossley
and Henry, 1990: 106; Smith and Karpinski, 1911: 4–5; see also Karpinski, 1915).
Incidentally, the word appearing just before al-jabr in the Arabic version of the title,
ḥisāb, though translated as calculation, also denotes arithmetic. Algorithm, arithmetic:
conceptually, they have been a stone’s throw away from one another since the 9th cen-
tury. Consequently, it is hardly the case that one corrupted the other. It is more accurate
to say that, until the second quarter of the 20th century, the arithmetic sense of the word
algorithm was not dominant or preferred.
The other key work is al-Khwārizmī’s untitled text on Hindu- or Indo-Arabic num-
bers, or what today many Westerners simply refer to as ‘Arabic’ numerals. It is widely
believed that this untitled manuscript of al-Khwārizmī’s played a major part in introduc-
ing Europeans to Arabic numerals in the middle ages (Crossley and Henry, 1990: 104).
Just as Al-Khwārizmī’s name became synonymous with arithmetic through the algebra
book, so too did it become synonymous with the Arabic system of numeration itself. The
form of the word algorithm that has today fallen out of favor, algorism, is a legacy of this
association. Until the early 20th century, Arabic numerals were commonly referred to as
‘the numbers of algorism’ (‘Algorism’, n., n.d.)
Still, this is not the only or most interesting sense of the term. Algorism’s semantic
context includes a range of secondary meanings that are key to making sense of algorith-
mic culture. Among the most important is its close association with zero (Smith and
Karpinski, 1911: 58). The word zero comes from śūnya, Sanskrit for ‘void’, which
migrates into Arabic as ṣifr, meaning ‘empty’, the root from which the modern English
language form cypher derives (Smith and Karpinski, 1911: 56–57). Thus, it is no coinci-
dence that the phrase ‘cypher in algorism’ was long used interchangeably with the word
zero; sometimes cypher would be used to designate any of the Arabic numerals, making
it synonymous with algorism (‘Algorism’, n., n.d., ‘Cipher, Cypher’, n., n.d.). Moreover,
until the middle of the 19th century, cypher, like zero, could refer to a placeholder – often
in a derogatory sense, indicating a ‘worthless’ person (‘Cipher, Cypher’, n., n.d.). This
was alongside what has emerged today as cypher’s more commonplace definition,
namely, a secret code or the key by means of which to crack it.
So, on the one hand, we have algorithms – a set of mathematical procedures whose
purpose is to expose some truth or tendency about the world. On the other hand, we have
Striphas 405
algorisms – coding systems that might reveal, but that are equally if not more likely to
conceal. The one boasts of providing access to the real; the other, like an understudy,
holds its place. Why in the early 20th century did algorithm become preferred over algo-
rism, so much so that the latter form is now all but an archaism?
In a word, information. The touchstones in this regard are two landmark papers, both
written by engineers who worked at Bell Laboratories in the United States. The first,
Ralph Hartley’s ‘Transmission of Information’, appeared in 1928. The second, Claude E
Shannon’s ‘Mathematical Theory of Communication’, appeared in 1948. Hartley’s paper
was noteworthy for many reasons, chiefly technical, but perhaps his most audacious
move was to subsume communication under the rubric of information. He states, ‘In any
given communication the sender mentally selects a particular symbol … As the selec-
tions proceed more and more possible symbol sequences are eliminated, and we say that
the information becomes more precise’ (Hartley, 1928: 536). Hartley thus conceived of
communication as a procedural activity – a game of chance in which the stake was infor-
mation, or the likelihood of achieving identity of message within and across a specific
context of interaction. He was followed in his work by Shannon, who raised the stakes
on Hartley’s theory.
One of the differences between Hartley and Shannon’s papers was that Shannon
placed significantly less faith in the process of communication. For Hartley, it was a rela-
tively placid affair in which the revelation of symbols led to understanding and order. For
Shannon, communication was a tumultuous affair consisting of such a complex ensem-
ble of determinations, both past and present, that disorder was the state to which it natu-
rally tended. Communication thus occurred within a context rife with uncertainty, or in
the language of information theory, entropy; order could not be taken for granted but
instead needed to be engineered. Shannon’s problem was thus to figure out how to parse
signal and noise, and thereby increase the odds that the system would achieve a sufficient
degree of order. Hence, he needed to devise a set of procedures – an algorithm, if you
will, although he did not use the term specifically – capable of dealing with the cascade
of determinations that governed communicative encounters. Shannon may have believed
he was developing a ‘Mathematical Theory of Communication’, but in fact he produced
among the first algorithmic theories of information.
It is worth mentioning that Shannon was not just a talented electrical engineer, he was
also a world-class cryptographer, having worked on several government-sponsored
‘secrecy’ projects at Bell Labs throughout the Second World War. During that time, he
produced a lesser-known paper, originally classified, entitled ‘A Mathematical Theory of
Cryptography’ (Shannon, 1945). Shannon operated, in other words, at the junction point
of algorithms and algorisms. Or, as he described his work on communication and cryp-
tography many years later, ‘they were so close together you couldn’t separate them’
(quoted in Kahn, 1967: 744; see also Gleick, 2011: 216–218). Indeed for Shannon, com-
munication in the ordinary sense of the term was nothing other than a special, simpler
case of cryptography, or of ciphering and deciphering. Both in his view consisted of
signals and noise stuck in a dizzying, entropic dance, along with telling redundancies
that, if exploited using the right mathematics, could mitigate much of the turmoil and
thereby point the way toward order (Rheingold, 1985: 119). What Shannon was essen-
tially proposing in his work, then, was the use of algorithms to attenuate algorisms.
406 European Journal of Cultural Studies 18(4-5)
Conclusion
I have tried my best to connect as many of the dots between the words information,
crowd and algorithm as possible. I realize, of course, that there are a great many dots left
to be connected – something that is inevitable in a synoptic piece such as this, which
marshals a version of what Franco Moretti (2005) has called a method of ‘distant read-
ing’ (p. 1). I am well aware of the prejudices of this research, moreover, particularly the
privileging of the work and experiences mostly of White men of European descent. The
purpose in doing so is not to exalt them. Instead, I am trying to tell a story about word-
worlds or ‘universes of reference’ they helped call into being by using specific terms
unconventionally (Guattari, 1995: 9). Having said that, I want to say a few more words
about how the conceptual history I have presented here connects up with culture, and
also then big data, data mining and analytics.
Matthew Arnold’s (1993 [1869]) Culture and Anarchy is infamous for having defined
culture in elite terms, as ‘the best which has been thought and said’ (p. 190). Elsewhere
in the book Arnold refers to culture as ‘sweetness and light’, thereby defining a model
disposition for the small group of apostles whom he imagined would, like him, spread
the gospel of culture. Yet, there is a third sense of culture present in the book which,
while hardly overlooked, is overshadowed by these two more quotable definitions. He
refers to culture as ‘a principle of authority, to counteract the tendency to anarchy which
seems to be threatening us’ (Arnold, 1993 [1869]: 89). For Arnold, culture as authorita-
tive principle meant a selective tradition of national – specifically English – art and lit-
erature that would, in his view, create a basis for national unity and moral uplift at a time
when heightening class antagonism was threatening English society.
This latter definition – culture as authoritative principle – is the one that is chiefly
operative in and around algorithmic culture. Today, however, it is not culture per se that is
a ‘principle of authority’ but increasingly the algorithms to which are delegated the task of
driving out entropy, or in Arnold’s language, ‘anarchy’. You might even say that culture is
fast becoming – in domains ranging from retail to rental, search to social networking, and
well beyond – the positive remainder resulting from specific information processing tasks,
especially as they relate to the informatics of crowds. And in this sense, algorithms have
significantly taken on what, at least since Arnold, has been one of culture’s chief respon-
sibilities, namely, the task of ‘reassembling the social’, as Bruno Latour (2005) puts it –
here, though, using an array of analytical tools to discover statistical correlations within
sprawling corpuses of data, correlations that would appear to unite otherwise disparate
and dispersed aggregates of people (see, e.g., Hallinan and Striphas, 2014).
I suggested at the outset of this article and at points along the way that part of what is
at stake in algorithmic culture is the privatization of process: that is, the forms of decision-
making and contestation that comprise the ongoing struggle to determine the values, prac-
tices and artifacts – the culture, as it were – of specific social groups. Tarleton Gillespie
(2011) has explored this issue in relationship to Twitter’s trending topics, noting how the
company’s black box approach creates all sorts of mystifications about how it adduces
topical importance. ‘The interesting question is not whether Twitter is censoring its Trends
list’, he writes. ‘The interesting question is, what do we think the Trends list is … that we
can presume to hold it accountable when we think it is “wrong”’ (Gillespie, 2011). His
Striphas 407
point is that Twitter and its kin bandy about in what one might call the algorithmic real,
where placeholders for trending topics and the like are presented as if they were faithful
renderings of reality. But the issue is even more complex than this. Gillespie (2011) adds
that ‘[w]e don’t have a sufficient vocabulary for assessing the algorithmic intervention in
a tool like Trends’, an observation that underscores just how deeply entangled are ques-
tions of language, technology, big data, analytics and political economy. This is all the
more reason to broach the issue of the privatization of cultural decision-making only after
having explored the semantic context, or keywords, that frame the issue in the first place.
In brief, consider the product recommendations one sees on Amazon. These, says the
retailer, are the result of one’s browsing and purchasing histories, which are correlated
with those of Amazon’s millions of other customers – a crowd – to determine whose buy-
ing patterns are similar to one’s own. You, too, might like what this select group has
bought, and vice-versa – a process Amazon calls, ‘collaborative filtering’. Google report-
edly works in a similar way. Although the company has moved far beyond its original
‘PageRank’ algorithm, which measured the number of links incoming to a website to
determine its relative importance, it still leverages crowd wisdom to determine what is
significant on the web. As Wired magazine explained in 2010,
PageRank has been celebrated as instituting a measure of populism into search engines: the
democracy of millions of people deciding what to link to on the Web. But Google’s engineers
… are exploiting another democracy – the hundreds of millions who search on Google, using
this huge mass of collected data to bolster its algorithm. (Levy, 2010: 99–100)
All this makes algorithmic culture sound as if it were the ultimate achievement of
democratic public culture. Now anyone with an Internet connection gets to have a role in
determining ‘the best that has been thought and said’! I am tempted to follow here by
saying, ‘much to the chagrin of Matthew Arnold’, but I am not convinced that algorith-
mic culture is all that far removed – in spirit, if not execution – from a kind of Arnoldian
project. Despite the populist rhetoric, I believe we are returning to something like his
apostolic vision for culture. The Wired magazine story from which I just quoted also says
this: ‘You may think [Google’s] algorithm is little more than a search engine, but wait
until you get under the hood and see what this baby can do’ (Levy, 2010: 98). The prob-
lem is, thanks to trade secret law, nondisclosure agreements and noncompete clauses,
virtually none of us will ever know what is ‘under the hood’ at Amazon, Google,
Facebook or any number of other leading tech firms. As Gillespie (2007) is fond of say-
ing, you cannot look under a hood that has been ‘welded shut’ (p. 222).
All this harkens back to the oldest sense of information – where some mysterious
entity is responsible for imbuing people and objects with shape, quality or character. I do
not mean to downplay the role that crowds play in generating raw data. Yet, it seems to
me that ‘crowd wisdom’ is largely just a stand-in – a placeholder, an algorism – for algo-
rithmic data processing, which is increasingly becoming a private, exclusive and indeed
profitable affair. This is why, in our time, I believe that algorithms are becoming deci-
sive, and why companies like Amazon, Google and Facebook are fast becoming, despite
their populist rhetoric, the new apostles of culture.
408 European Journal of Cultural Studies 18(4-5)
Funding
This research received no specific grant from any funding agency in the public, commercial or
not-for-profit sectors.
Notes
1. This is not to suggest algorithmic culture is somehow strictly computational and therefore
exclusive of human beings. As Tarleton Gillespie (2014) has noted, and as the preceding
example suggests, algorithms are best conceived as ‘socio-technical assemblages’ joining
together the human and the nonhuman, the cultural and the computational. Having said that,
a key stake in algorithmic culture is the automation of cultural decision-making processes,
taking the latter significantly out of people’s hands (Flusser, 2011: 117).
2. Galloway does not offer a specific definition of ‘algorithmic culture’, nor does he provide any
type of genealogy for the term. His having largely taken this suggestive idea for granted is a
primary motivation for this essay.
3. Outside the United States, the book is simply titled, Culture.
4. Several of these terms appear in Fuller (2008), although the project does not adhere closely
to a Williamsonian keywords approach. The Williams-inspired New Keywords (Bennett
et al., 2005) contains only a handful of them. Ben Peters’ (ed.) forthcoming Digital Keywords
project is the most compelling project to have developed in this vein to date (Welcome, n.d.;
see also Striphas, 2014).
5. Beyond Williams’ passing interest in information, I can offer no strong empirical basis for the
selection of these terms beyond my own intuition, or a desire to engage in a thought experiment
that would attempt to see what new understandings of culture might emerge from having placed
the word alongside information, crowd and algorithm. That said, one should not dismiss ‘intui-
tive’ methods as lacking in scholarly rigor. Henri Bergson (1992), for one, pioneered the project
of recovering intuition from the Kantian doctrine of the faculties, seeing it as a way of relating
to the world that was less categorical and therefore better attuned to duration (pp. 126–129).
More recently, Lauren Berlant (2011) has made a strong case for the relationship of intuition,
the somatic and the affective (pp. 52–53). Gregory J Seigworth (2006) also gets at the point in
arguing for the relationship between intuition and what Williams has called the ‘pre-emergent’,
which is to say a category of experience exceeding the realm of the visible and the articulable.
It is also not a coincidence that Seigworth draws attention to the etymological links between the
words experience, experiment and empiricism (Seigworth, 2006: 107–126; Williams, 1977: 132).
6. The exception here would be the word’s usage in the phrase ‘the usual crowd’, which indi-
cates familiarity with those who have assembled.
7. Williams (1983) notes that, prior to this time, the word multitude tended to predominate in
English. It is the 18th and 19th centuries that see the rise of mass and, evidently, crowd as well
(p. 192).
8. On Olson’s friendship with Hayek, see p. viii.
9. For a critique of the politics of platforms, see Gillespie, 2010.
10. Although hardly a prevalent term in the English language, the word algorithm’s usage shows
a dramatic upsurge from about 1960 on. Before that year, it barely registered, but between
1970 and 2000 its usage increased by about 350 percent, approaching levels comparable to
crowd (‘Algorithm’, n.d.).
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Biographical note
Ted Striphas is Associate Professor in the Department of Communication and Culture, Indiana
University, USA. He is the author of The Late Age of Print: Everyday Book Culture from
Consumerism to Control (Columbia University Press, 2009) and is currently at work on his next
book, Algorithmic Culture. Twitter: @striphas.

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