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Artificial intelligence in popular culture


David (22) who lives in Vancouver, Canada, always checks cultural content on digital platforms, in particular his Apple iPhone. After watching a movie trailer of Morgan—created through artificial intelligence—a 2016 American sci-fi horror film on YouTube in March 2020, he immediately enjoyed the movie on Netflix. Right after, he also watched Kingdom—a Korean television series produced in 2019—on Netflix, partially due to social distancing regulation in the COVID-19 era.

Three major components that directly influence David’s cultural activities at this particular juncture are artificial intelligence (Al); digital platforms, including Netflix and smartphone; and popular culture. The interaction among these three seemingly not connected areas has been greatly increasing, a relatively new development, as Al has recently jumped into the realm of media and popular culture. Although Al is the latest comer in the cultural sphere, it has suddenly become one of the major forces transforming people’s cultural consumption habits, as well as the production of popular culture.

Al has been with us for many years, and the huge wave of Al breaks across several areas, such as robots, self-driving cars, Google Maps, Amazon, healthcare, and online education. In early 2020 when COVID-19, referring to an infectious disease starting in late fall or early winter 2019, was rampant globally, for example, the Canadian federal government signed a contract with BlueDot, a Toronto-based digital health firm to track the spread of the virus, the latest tool in the Al toolbox being deployed against the public health crisis. The federal government announced a CDN$1 billion COVID-19 response fund on March 11, 2020, with $275 million dedicated to research. A little less than $52 million has already been doled out to the 96 researchers and research teams across the country, and three of those projects are using Al (Chamandy, 2020).

Several information technology (IT) firms and universities have also developed not only online education tools but also advanced technological systems that subtract students’ participation and concentration on online education via Al technologies (Choi, J.H., 2020). Due to the rapid spread of the disease, universities in many countries, including the U.S., the U.K., Canada, and Korea, canceled in-class education and introduced online class systems. As the COVID-19 situation continues, and the new normal in the post-coronavirus era is evident, many universities and tech companies have continued to develop various forms of online education tools, mainly supported by AL In other words, the rush to adopt new digital technologies during COVID-19-driven remote learning led educators at all levels, from elementary school to university, to use more tools powered by advanced Al (Rauf, 2020). As of late January 2021, many universities around the world still practice remote learning, which was started in March 2020, and therefore, Al-supported educational mechanisms have continued to deeply embed in people’s daily lives in the near future.

Meanwhile, Al is saturating in the realm of popular culture, in which humans have been traditionally primary actors. Many AIs now “act invisibly in the background of activities conducted on smartphones and computers; in search engine results, social media feeds, video games and targeted advertisements” (Dyer-Witheford et al., 2019, 2). As Elliott (2019, xx) points out, “The digital universe has a direct connection with AL” Furthermore, today’s changes in the field of popular culture are far more encompassing in scope than Al alone as digital platforms; both social media platforms like YouTube and over-the-top (OTT) service platforms like Netflix also play a major role in the transformation process in conjunction with popular culture. Consequently, people around the globe live in a new cultural world of technological innovation that Al and digital platforms create and develop.

Since the mid-2010s, Al has been applied to quite diverse applications, including predictive text assistance in smartphones, the identification of objects and faces for photos, the interpretation of video material linked to self-driving cars, the evaluation of performances recorded as data, and many more—“a list which shows that the technology of machine learning is being used in multiple sectors and for a broad range of activities, some sensitive and others more playful” (Bunz, 2019,264). The swelling use of Al in media and culture in tandem with digital platforms, however, came unplanned with a few exceptions, right after Korea hosted a Go match telecast live where Google DeepMind’s AlphaGo defeated Lee Sedol, one of the best Go players in the world, in 2016 (Figure 1.1).1 The AlphaGo Al program triumphed in its final game against Lee to win the series 4-1, providing a landmark achievement for an Al program (Borowiec, 2016; Choi, W.W., 2016).

As evidenced in the Go match between AlphaGo and Lee Sedol, Al can easily beat humans in Go and chess. Watching AlphaGo’s victory over Lee put people, not only in Korea but also around the globe, into shock, as many global GO fans watched those five matches.2 Everyone was suddenly talking about Al, and many global cultural and platform firms started to invest in this particular new technology. All this is in anticipation of the “Fourth Industrial Revolution,” which “proponents say differs from the third digital

Go Match between AlphaGo and Lee Sedol in 2016 (SBS, 2016)

Figure 1.1 Go Match between AlphaGo and Lee Sedol in 2016 (SBS, 2016)

revolution in its emphasis on Al technology.3 Some speculate this could affect the digital economy as much as its shipping, automobile and electronics industries” (Volodzko, 2017). Al has seemed to become a core technology in the media and cultural industries in the age of the Fourth Industrial Revolution (Kim, K.H. et al., 2018).

Starting in the late 2010s when they saw the AlphaGo phenomenon, the global cultural industries, as in other industries, have rapidly transformed their industrial structures and the ways in which they produce cultural content. In both advanced economies, including the U.S., the U.K., Canada, and Japan, known as the Global North, and a few developing countries like China and Korea, namely, the Global South, Al, algorithms, and big data are reshaping all media-related industries, from platform to cultural sectors, as new digital technologies, including Al and digital platforms, are expected to provide opportunities for many ICT (information and communication technology) firms to create jobs and engender prosperity (McKelvey and MacDonald, 2019). These countries have already achieved substantial growth in their ICTs—internet and smartphones—and cultural industries. Mega media and cultural giants in these countries now focus on Al to further develop and produce new forms of popular culture, and therefore, the digital economy.

Al is becoming a ubiquitous part of our cultural lives, and the adoption of Al is indeed noticeable in media and cultural sectors like music, film, game, and webtoon, as well as journalism. As Caramiaux et al. (2019, 6) point out, “Creatives have always been in demand of new tools that they can use to enrich the way they work, making them early adopters of technological innovations. Al is not an exception.” Al seems to be suited to the particular requirements of the contemporary cultural industries that are shifting principal paradigms. In particular, Al is deeply associated with digital platforms, such as Google, Facebook, YouTube, Netflix, and smartphone technologies.

The paradigm shift in the realm of media and culture in tandem with Al has been controversial. Some admit that Al is a big part of the cultural sector, while others claim that it is only lip service. What is interesting, though, is that there has been a clear trend toward the adoption and actualization of Al in media and cultural production. As Gunkel (2012, 20) argues, in communication studies,

the operative paradigm—the framework that has defined what is considered normal science—situates technology as a tool or instrument of message exchange between human users. This particular understanding has been supported and codified by the dominant forms of communication theory. . . . Because this conceptualization has been accepted as normative, the computer and other forms of information technology have been accommodated to fit the dominant paradigm.

What he emphasized is that the computer has not effectively challenged longstanding assumptions about the role and function of technology in communication. For him, “The computer is not necessarily a new technology to be accommodated to the theories and practices of communication studies,” but the current situation has fundamentally changed (Gunkel, 2012, 20).

Now, it is certain that the computer and Al have actualized a paradigm shift in digital media and popular culture. The emphasis has switched to machine learning (ML), meaning a subset of Al, in communication and/ or cultural studies. “With huge datasets available and cheaper hardware, machine learning has been gaining ground over conventional search and discovery applications. The future lies in machine learning and it is maturing at a faster pace and finding diverse applications” (Frankel, 2018,10). As Benchmann (2019, 82) points out, data in ML processing becomes an issue not only in terms of the quality of data input itself but also its suitability for training the algorithms to recognize patterns and clusters. The more data and the more diversified training data people have, the better people’s algorithm potentially is at recognizing new data. Here the algorithm can only interpret data and predict patterns from the data that it has already seen from training data.

Many governments around the world have embraced the Al phenomenon. They firmly believe that Al can reshape the industry structure and is able to bring new energies to the digital economy. Governments have developed new supporting measures, both legal and financial, so that governments and corporations can work closely together to enhance the AI-driven digital economy. Governments, digital platform firms, and cultural corporations like broadcasting companies and music firms discuss a whole host of agendas surrounding the remarkable emergence of Al in general, as

Artificial intelligence in popular culture 5 our contemporary society is “becoming increasingly visible and bullish on its own investments in Al” (Walch, 2019). The media landscape has been transformed so deeply that it is now unrecognizable. For many governments and corporations, how to utilize new digital technologies, in particular Al, for the growth of popular culture, and in general cultural production, becomes a key issue.

Cultural production can be explained from various perspectives, and it means “the making, circulation and reception of cultural forms and to cultural practices and processes in situ”; therefore, cultural production, sometimes, implies “processes whose outcomes or products are specialized and well defined” (Henderson, 2013, 3).4 In cultural production studies, it has also “addressed industrial contexts and professional routines to understand why films, television programs, and popular music genres are what they are” (3). However, in this book, cultural production refers to “the social processes involved in the generation and circulation of cultural forms, practices, values, and shared understandings” as well as “the work of the culture industry” (Oxford Reference, 2019). Cultural production is also “used as a shorthand term to refer to industrialized or semi-industrialized symbol making and circulation in modern societies” (Hesmondhalgh and Saha, 2013, 181). In other words, cultural production does not narrowly define the actual production of cultural content, but the overall process, including production, distribution, exhibition, and consumption of media content and popular culture and, recently, embedded in Al use.

Due to the significant role of digital platforms and their users who also participate in the process of cultural production and circulation, this book broadly includes the digital platform industry as well as the cultural industries as major parts of cultural production.

Digital platforms and cultural industries have transformed the production and distribution of cultural content, while people shift their consumption habits with the help of Al and digital platforms, differentiating the cultural sphere from the traditional norms that traditional media, including terrestrial broadcasting, have created. In the early 21st century, people, in particular global youth, equipped with new digital technologies enjoy popular culture and news on digital platforms instead of physically going to theaters or buying and possessing cultural materials.

On the one hand, recent data certainly proves the increasing use of Al in the ICT and cultural sectors, which asks us to contemplate the convergence of Al and platform/culture in the production of culture. According to Statistics Canada (2020), several different industries have invested and used Al technology. Among these, the information and cultural industry was the second highest in terms of Al usage at 25.5%, only behind the finance and insurance industry (32.2%), while the average usage of Al was recorded at 10.1% in 2017 (Table 1.1). This data includes only large companies who have more than 250 employees. The ranks are still similar when we include small businesses, although the percentage of Al usage slightly decreases.

Table 1.1 Al usage by industry in Canada in 2017 (unit %)

Source: Statistics Canada (2020). Table 27-10-0367-01 Use of advanced or emerging technologies by industry and enterprise size.

This certainly implies that IT, including digital platform and mobile sectors, and culture have already deeply integrated with Al technology, and we can expect that new media and cultural sectors will continue to use Al at the highest level in our contemporary capitalist society.

On the other hand, Al has greatly transformed people’s consumption patterns in media and popular culture, as people enjoy media and cultural content on digital platforms, including social media platforms and OTT platforms (e.g., Netflix), rather than on traditional media like television channels and at theaters. People watch movies and listen to music recommended by Al and algorithms on Netflix and Spotify. As Frankel (2018, 10) argues, as user interfaces have advanced to

help TV viewers more easily find content amid an ever-expanding field of choices, the data science used to achieve true personalization in many cases has moved beyond the scope of human capabilities. The blending of voice recognition into the user search and recommendation process only serves to further the need for AL

The rise of digital platforms, such as Facebook, YouTube, and Netflix is vital “in the transformation of social into data-minable business ventures,” which means that people enjoy popular culture through digital platforms backed by machine learning and algorithms that recommend cultural

Artificial intelligence in popular culture 7 content to individual users (Langolis et al., 2015, 4). As Lobato (2019, 40) also aptly puts it, one major characteristic of digital platforms is “a reliance on algorithmic recommendations.” Netflix, for example, has played a major role in “the development and popularization of recommendations” since its inception. Several OTT platforms, including Netflix, Disney+, and Amazon Prime, and music streaming service platforms, such as Spotify and Apple Play, function as contemporary distribution platforms and connect production and consumption.

However, new digital technologies, including Al and algorithms, have raised some serious concerns, as several global mega platform giants like Facebook and Netflix preoccupy and utilize them to make huge profits, resulting in socio-economic disparities between platform owners and platform users (Fuchs, 2014; Jin, 2015; Srnicek, 2016). While providing extraordinary opportunities, contemporary technological innovation driven by Al has brought about unprecedented risks.

As one of the major caveats, several researchers (Barocas et al., 2019; Reis-man et al., 2018) argue that the rapid growth and use of Al have brought about potential cultural and social problems—so-called representational harms—to our contemporary society. This particular harm occurs because Al requires the agency of people who adopt Al

to make assumptions or predictions about cultural or social factors that vary enormously within and between communities and geographic areas. This practice often results in findings only reflecting potential impacts on a dominant culture and omitting or misinterpreting the impacts on marginalized communities and individuals.

(Reisman et al., 2018, 18)

Compared to allocative harms, which are caused when a system withholds an opportunity or resource to certain groups, representational harms occur when agencies and systems reinforce the subordination of some groups along the lines of identity—age, class, ethnicity, gender, and so on (Barocas et al., 2019):

Representational harms have long-term effects, and resist formal characterization. But as machine learning becomes a bigger part of how we make sense of the world—through technologies such as search, translation, voice assistants, and image labeling—representational harms will leave an imprint on our culture, and influence identity formation and stereotype perpetuation. Thus, these are critical concerns for the fields of natural language processing and computer vision.

(Barocas et al., 2019, 30-31)

Meanwhile, globally, it is not surprising to learn that leading platforms, including Google, Facebook, and Netflix, armed with big data, algorithms, and Al are characterized “by an avid tendency to colonize and converge into ever-new markets” (Schwarz, 2017, 384). Many governments and corporations seem to advance some mechanisms to secure socio-economic fairness and equality; however, these measures are not practical, nor transparent, in the majority of countries, whether in the Global North or in the Global South.

Major goals of the book

Media scholars, sociologists, cultural anthropologists, and computer scientists have explored the emergence of Al, algorithms, and big data in tandem with media and culture. Several existing books (Langolis et al., 2018; Andre-jevic, 2020; Gunkel, 2020) address the roles of Al and big data; however, there is no single book discussing the convergence of Al, digital platforms, and popular culture other than digital games (Yannakakis and Togelius, 2018; Togelius, 2019). For example, in The Culture of Al, Elliott (2019) discusses the ways in which intelligent machines, advanced robotics, accelerating automation, and big data5 impact people’s day-to-day lives and contemporary societies. His understanding of the reordering of everyday life highlights the centrality of Al to everything people do—from receiving Amazon recommendations to requesting Uber and from getting information from virtual personal assistants to talking with chatbots. These academic books on Al are valuable sources, as they offer intriguing case studies of and/or theoretical discussions about new media technologies, Al in this case.

Nevertheless, these books have provided insufficient comprehensive studies of the use of Al in media and popular culture. When they did, they tended to focus on a particular cultural genre—namely, gaming—rather than examining this cultural trend involving various cultural genres, such as music, variety shows, TV dramas, and films. Thus, they could not adequately examine how structural factors and audience engagements interplay in the rise of AL In other words, Al is currently discussed and used everywhere in the media; however, its impact on popular culture is rarely analyzed. In particular, these studies did not focus on the nexus of Al, digital platforms, and popular culture. Some scholars in several fields seem to understand the convergence of these areas but rarely focus on the nexus of these areas, given the short history of the subject matter.

Unlike previous works, this book offers a comprehensive yet critical understanding of Al as a major force in cultural production, which means that it explores cultural production in the age of AL I explore the impact of Al on culture, focusing on the situation as it relates to cultural creators, the cultural industries, and the public as consumers, in both the Global North and the Global South, “at a time when the large digital platforms are taking over bigger chunks of the value chain” (Kulesz, 2018a, 5). This book not only discusses several major exemplary cultural forms, including music, gaming, and webtoon as the format of the nexus of Al and culture, but also offers a critical understanding of the convergence of Al, digital platforms,

Artificial intelligence in popular culture 9 and popular culture. This book’s evidence-based analyses of Al in the media and cultural industries from a political-economy perspective make significant theoretical and empirical contributions to the literature in several academic fields, including media and cultural studies, science and technology studies (STS), and globalization studies. In so doing, it will also put forward new ideas on the ongoing Al phenomenon.

This book, as the first academic discourse on the conjunction of Al, digital platforms, and popular culture, attempts to understand the ways in which the digital platform and cultural industries have reshaped and developed AI-driven algorithmic cultural production, distribution, and consumption. As can be seen throughout the book, with the emergence of big data, algorithms, and Al, cultural industries have fundamentally transformed their business models and cultural production formats in order to maximize the benefits from these cutting-edge technologies, as well as to appeal to audiences who are tech-savvy and delicate consumers. In the age of artificial intelligence, this changing media ecology demands that we examine the cultural production of popular culture from new perspectives through our understanding of the nexus of Al, digital platforms, and popular culture. Furthermore, Al-driven cultural transformation has required us to adequately grasp contemporary society’s socio-cultural, economic, and political spheres, which play pivotal roles in the process.

To begin with, this book examines the ways in which media and cultural industries utilize Al and algorithms to advance the new forms of cultural production, including the production of culture, distribution, and exhibition. By selecting a few major media and cultural industries that use Al and big data the most, such as the music, game, and webtoon sectors, as well as Al journalism, it discusses how these new digital technologies influence the transformation of cultural production. This part of the discussion also includes the role of Al and algorithms in both distribution and exhibition. Due to the recent surge of some local cultural content, including Korean popular culture in the global markets, and local cultural firms’ endless efforts to develop popular culture in tandem with Al and big data, the discourses embedded in the convergence of Al, digital platforms, and popular culture focus on the particular local sphere both in the Global North and the Global South. However, as these new technologies and cultures are transnational, the analyses are naturally global, which means that it interprets the discourses between the global and the local so that readers can comprehend several major characteristics in the production of culture.

Second, it investigates change in cultural consumption as part of cultural production by analyzing the ways in which Al and digital platforms reshape people’s consumption habits, including in relation to COVID-19. As digital platform firms and cultural corporations who develop Al and algorithms supported by big data are mega giants, it maps out how global platform giants transform local platforms, and therefore, consumers. In fact, Nieborg and Poell (2018, 4279) focus on “the inherent accumulative tendency of capital and corporate ownership and its subsequent effects on the distribution of power and the precarious and exploitative nature of cultural and (immaterial) labor of both producers and end-users.” Subsequently, this book maps out whether new business models protect people’s preferences and diversity in culture. The analysis in this particular perspective certainly examines the power relationships between platform firms who use Al and big data and the users who do not have Al technology, but consume recommended cultural content. “Al’s purpose can loosely be summarized as algorithmically-driven automation that drives the improvement of service from both a business and user perspective”; therefore, it is critical to understand the Al-driven digital platform environment from both production and consumption sides (Easton, 2019).

Third, as an extension of the discussion above, it maps out the transformation of people’s consumption habits toward personal culture. As AI-equipped digital platforms have advanced “personal culture,” compared to mass culture, it addresses the nature of the personalization of popular culture. In a media context, digital platforms supported by Al and algorithms like Netflix have recommended particular programs to individual users so that these audiences consume popular culture personally and selectively, which characterizes contemporary cultural scenes. Many digital platforms, from social media platforms to OTT platforms, “aim for increased levels of personalization” (Pangrazio, 2018, 12). Personal culture, as one of the most distinctive characteristics of the contemporary cultural sphere, can be referred to not only as popular culture and media-produced and recommended by Al-equipped cultural producers and digital platforms but also as cultural consumption conducted individually on and through digital platforms, including social media platforms. Since Al and algorithms in the realms of popular culture and media have developed personalized recommendation systems, personal culture implies the entire process of cultural production, distribution, and consumption in the age of digital platforms.

Fourth, it documents and discusses governments’ own rapid developments of Al-relevant policies in cultural production, as Al interlaces with the nation-state, the contemporary phase of globalization, and geopolitics. In particular, it draws on the active engagement in the process and builds on the legacy of digital platforms and cultural policies. Governments around the globe fiercely invest in Al and big data, as they are drivers for the growth of the digital economy. The governments have developed new policy measures through their legal and financial arms in order not to be left behind. Therefore, this book does not only identify key policy issues relevant to Al in tandem with media and popular culture, but it also attempts to critically interpret major issues occurring in the early 21st century.

Last, but not least, as a continuation of the previous discussion, it examines whether governments and corporations have advanced reliable public and corporate policies and ethical codes to secure socio-economic equality. It is not clear whether Al and big data achieve socio-cultural progress in

Artificial intelligence in popular culture 11 addition to economic prosperity. As one of the most significant concerns in our contemporary cultural environment is the disparity between the haves and the have-nots, as well as fake news on social media platforms, it discusses whether new ethical codes are able to develop socio-economic justice and equality so that we may contemplate the future of the Al-driven media and cultural sphere. In addition, it examines whether Al embedded in popular culture advances diversity and cultural identity, which are crucial components of democracy in the realm of popular culture.

As a major analytical framework, this book employs critical political economy, as it is important to explore the relationship between technological development and the political economy of contemporary culture. There are high stakes in debates over the use of digital technologies in cultural industries; therefore, it is significant to investigate the relationship between technological development and growth, mainly Al and digital platforms in this book, and the political economy of contemporary media and culture. Unlike other approaches, political economy gives priority to comprehending “social change and historical transformation.” This means that people need to understand “the great capitalist revolution, the upheaval that transformed societies” (Mosco, 2009, 26). As political economists are also concerned about power and politics, it takes a historical and normative approach toward the growth of digital technologies, including Al and big data (Mosco, 2009). As Susan Strange (1994, 125) already pointed out, digital technology, including Al, has been “made to serve the interests of the state and to reinforce its power.” Therefore, policy mechanisms and Al initiatives must be understood “from a critical perspective that considers development, deployment, and impact from a wide diversity of voices beyond the tech sector” (McKelvey and MacDonald, 2019,44). Digital platforms like Facebook and Netflix use Al and algorithms to assemble databases about customers and to target campaigns “based on information gathered through their surveillance.” Surprisingly, users are “often willing to exchange personal information for specialized services,” which consequently allows major digital platforms supported by Al to expand their revenues (Klinenberg and Ben-zecry, 2005, 9). In particular, in an ever more Al and digital platform-centric world, the media and cultural infrastructure industries and their close relationship with popular culture are now the center of gravity around which our contemporary society and culture revolve (Winseck, 2016).

As such, this book discusses relevant questions from a critical politicaleconomy perspective that emphasizes not only power relationships between politics and the economy but also between cultural creators and consumers. In other words, it is critical to discuss the role of Al in shifting production and consumption patterns in the context of media and culture, because it brings new imperatives for understanding power relations between two major actors—cultural creators and cultural consumers—within as well as outside of traditional state/market disparities (Youngs, 2007). This critical political-economy approach in the age of Al and digital platforms, in particular, in tandem with the coronavirus pandemic, will shed light on our current debates on the convergence of Al, digital platforms, and popular culture to determine the dominant power of Al and digital platforms in the vicious circle of cultural production in the near future. This approach also allows readers to critically ponder whether Al and digital platforms have developed cultural diversity in terms of the creation and preservation of plural ideas and diverse cultural tastes and, therefore, cultural democracy in the early 21st century.

Organization of the study

The study is organized as follows. After providing the fundamentals of the book in this chapter, the following chapters discuss detailed information, key issues, and future directions. In Chapter 2, I historicize and theorize the convergence of Al, digital platforms, and popular culture. Given their short histories in the realms of media and culture, it is necessary to define the major characteristics of Al and digital platforms, which also becomes a foundation for the convergence of Al and popular culture. In this chapter, I first construct how the histories and concepts of Al and digital platforms were originally developed for the media and cultural research. I especially discuss the ways in which we understand Al in the realms of media and popular culture. Then, I address the role of digital platforms in cultural production. Finally, I develop the ideas of convergence between Al and digital platforms in the cultural sector in order to understand the nexus of Al, digital platforms, and popular culture as one of the most significant trends in the early 21st century. The final part focuses on the theoretical development of our perspectives on the possibility of Al as a digital platform.

Chapter 3 investigates state-led Al policies in tandem with the media and cultural industries. It compares and contrasts Al policies between the Global North and the Global South, which eventually provides the discourses on AI-related policies in the cultural industries. The chapter discusses the reasons why these countries have adopted Al, big data, and algorithms as the drivers of popular culture and, therefore, the digital economy, both nationally and globally. As several leading countries in the Global North have continued to advance neoliberalism, while a few countries in the Global South have focused on developmentalism to support AI-related technologies and businesses, I offer some vantage points to compare relevant policy standards in conjunction with AL In particular, it examines whether governments in several countries must continue their emphasis on either neoliberal tendencies or developmentalism in the age of Al. Most of all, it discusses the possibility of a human-centered norm in the cultural sector in the Al era.

Chapter 4 examines the nexus of popular culture and Al in production. Al is one of the most versatile technologies that can be utilized in almost the entire process of game development, including production and data analytics. By addressing the complexity and specialty of the convergence of Al and

Artificial intelligence in popular culture 13 popular culture, this chapter attempts to offer new insights on the global cultural industries that vehemently work with Al and big data. It examines the nexus of popular culture, such as films, music, digital gaming, and webtoon and Al in production. While admitting the significance of Korean popular culture, including music (K-pop) and webtoons in the global cultural markets, it especially explores some Korean cultural sectors as among the most important cultural industries to converge Al and popular culture. It maps out how local entertainment houses have partnered with Al companies; therefore, it compares Al-supported cultural production between countries in the Global North and countries in the Global South. Finally, it discusses whether the encounters of Al and popular culture have advanced cultural democratization and creativity or not and whether audiences have enjoyed the benefits of Al-driven cultural production. There are high stakes in this particular dispute over the use of Al in cultural production and cultural industries; thus, it is critical to examine the relationships between Al development and the political economy of contemporary popular culture.

In Chapter 5, I explore the convergence of Al, algorithms, and digital platforms, focusing on Netflix, and in general OTT platforms. Netflix has played a key role in transforming the global entertainment markets. Having mostly conquered the Western markets, including Canada (2010) and the U.K. (2012), Netflix has pivoted east and vehemently sought to rack up its number of subscribers from Asia starting in 2015. Netflix entered Japan in 2015 and has a presence in most Asian countries. Young and increasingly digital populations in Asia present an incredible opportunity to ramp up Netflix’s international subscribers, and subscriptions in Asia already surpassed that of the U.S. at the end of 2018 (Gilchrist, 2018). The chapter first discusses the major characteristics of Netflix as one of the most significant OTT platforms, one that controls the vicious chain of the broadcasting and film industries. Second, it examines the ways in which Netflix, utilizing Al and algorithms, influences the content production industry in the global cultural industries, as the content industry has rapidly shifted its methods of production and distribution by learning from the Netflix model. Then, it investigates Netflix’s effects in the global OTT industry, including the Korean market. Through these discussions, it eventually articulates whether Netflix actualizes an asymmetrical relationship of interdependence between the West, primarily the U.S., and many developing countries, based on its crucial role in reshaping global platforms.

Chapter 6 examines the crucial role of consumers in the age of artificial intelligence and big data. As is well known, several digital platforms like OTT services, including Netflix and Amazon Prime, and music streaming service platforms, such as Spotify and Apple Play, connect production and consumption. While these digital platforms and cultural industries have transformed their production and distribution, people have also shifted their consumption habits with the help of Al and algorithms. In the early 21st century, global youth equipped with new digital media enjoy popular culture and news on digital platforms instead of physically going to theaters or buying and possessing cultural materials.

However, these consumers sometimes function as free labor—working as non-wage workers in return for their use of digital platforms—which triggers a severe disparity between platform owners and platform users. Understanding the concept of users as free labor is a crucial step in developing well-balanced approaches in the realm of digital platforms and popular culture. Then, it discusses the personalization of culture directed by Al so that people can understand Al’s impacts on people’s consumption trends. This chapter, therefore, investigates not only the transformational force of Al in production but also in consumption.

Chapter 7 explores the convergence of journalism and digital technology, focusing on Al, as it has especially transformed the journalism industry. In particular, it raises the question of fake news in tandem with digital platforms, such as Facebook, Twitter, and TikTok, as much of the fake news circulates on social media platforms. In fact, one of the most existential questions of the digital age is about the ways in which social media platforms are caught so unprepared for the rise of fake news, misinformation, disinformation, and digital falsehoods, which eventually work as tools of democratic destruction. This chapter maps out the implications of Al and big data in the realm of journalism, which must be foregrounded in the larger context of the digitization of media—a transition toward algorithms and social media—and, therefore, examines the ways in which Al and big data have transformed journalism as an institution (Lewis, 2019). By raising the question of what Al means for journalism, it aims to discuss how Al and big data have transformed the journalism landscape as we have known it. Al technologies, regardless of how transformative they prove to be in the long term, might be understood as part of a broader story of journalism’s reconfiguration in relation to new data and computer-driven systems (Lewis, 2019). In particular, this chapter attempts to analyze digital platforms, such as Facebook and TikTok as some of the most significant news platforms, which often produce and disseminate fake news. By discussing the plat-formization of journalism, it critically discusses the increasing role of Al in producing a news culture and its effects in journalism.

Chapter 8 discusses new media ethics in the age of AL Governments and media corporations around the globe seem to advance mechanisms to secure socio-economic fairness. By emphasizing social security, transparency, and accountability, these standards underscore whether Al-driven industrial policies present any biases or produce reliable results and ethical frameworks for society. However, due to Al’s fast and recent growth, these measures are not practical or transparent. Thus, this chapter addresses whether governments and corporations have advanced reliable ethical codes to secure socio-economic equality. It answers these questions from a critical political-economy perspective which emphasizes not only power relationships between politics and the economy but also socio-economic justice and

Artificial intelligence in popular culture 15 fairness. Chapter 9 summarizes the major characteristics of the convergence of Al, digital platforms, and popular culture and discusses several implications in conjunction with COVID-19 to provide some foreseeable topics to be addressed in the near future.


  • 1 Go is a game of two different players or teams who take turns putting black or white stones on a 19 x 19 grid. Players win by taking control of the most territory on the board. Meanwhile, AlphaGo is a computer program to play the Go game. AlphaGo’s algorithm uses a combination of ML and tree search techniques. AlphaGo has built up its expertise by studying recorded games and teasing out patterns of play {BBC News, 2016).
  • 2 In December 2019, when Lee Sedol finally retired, he expressed that he would no longer play professionally, partially because Al is impossible to overcome. During his interview with the media, he said, “with the debut of Al in Go games, I have realized that I am not at the top even if I become the No. 1 through frantic efforts. Even if I become the No. 1, there is an entity that cannot be defeated” (Webb, 2019).
  • 3 Simply put, the Fourth Industrial Revolution refers to how technologies like Al, autonomous vehicles, and the internet of things (loT) merge with humans’ physical lives. These technological changes are drastically altering how individuals, companies, and governments operate, ultimately leading to a societal transformation (Schulze, 2019). This Fourth Industrial Revolution is fundamentally different from previous revolutions, as it is characterized by a range of new technologies, including Al, that are fusing the physical, digital, and biological worlds, impacting all disciplines, economies, and industries (Schwab, 2016, 12).
  • 4 Although it is not perfect, Bourdieu (1983) already argued that to understand a work of art, people must look not only at the piece of art itself but also at the conditions of its production and reception, because these socio-cultural environments characterize the field of cultural production and, therefore, people can determine the way in which the production of culture, back then, mainly literature and pure art, relates to the wider fields of power and class relations.
  • 5 The term ‘big data’ appeared in the late 1990s, and big data mainly refers to “data sets whose size or type is beyond the ability of traditional relational databases to capture, manage and process the data with low latency. Big data has some common characteristics: nigh volume, high velocity or high variety. Al, mobile, social and the loT are driving data complexity through new forms and sources of data. For example, big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media—much of it generated in real time and at a very large scale” (IBM, 2020).
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