Political economy of the convergence of Al and popular culture
Cultural industries corporations and digital platforms in both the Global North and the Global South have passionately pursued Al-driven cultural developments. For them, it is critical to utilize Al, big data, and algorithms to create new cultural products that attract new generations who are equipped with new digital culture rooted in AL Consequently, the increasing role of Al in the media and cultural industries is becoming clear. Although still in its early stage, Roxborough (2019) argues that OTT services equipped with Al and algorithms have already moved into the mainstream in the media and cultural markets and are set to become the main destinations for video viewing around the world. Traditional media, including broadcasters and film companies, need to evolve new services to reflect the viewing preferences of contemporary consumers. It is essential for them to use Al to circulate cultural content, and they also need to develop cultural content supported by AL In the future, more cultural products will be produced through Al-related technologies, and cultural creators and corporations are afraid of being left behind, if they are not aggressively leading the trend. As Manovich (2018) points out, again, Al recommendation engines suggest what people watch, read, and listen to. Digital devices and services automatically adjust to the aesthetics of captured media. Furthermore, during the COVID-19 era, the traditional norm in cultural products has
Al and cultural production 67 been transformed, as many content producers work from home, which demands the increasing role of AL In March 2020, Netflix’s VP of networks Dave Temkin indeed claimed, “Right now, it’s not unique to us, most content production is shut down around the globe.” Therefore, our team “is working on ways to restart aspects of content production like post processing, visual effects and animation—things traditionally not done from home because it requires a significant amount of compute power and bandwidth to move raw encodes around” (Condon, 2020). What he emphasized was the ways in which digital platforms like Netflix get that operating out of people’s homes, and it is certainly pinpointing the massive use of Al-related technologies in the production process.
However, cultural production supported by Al has raised some concerns mainly due to several political-economy issues relevant to cultural production. To begin with, Al-supported cultural production blurs the protections from copyright law, as the producers are not only humans but also computers. In the pre-AI era, the work of cultural creators had been protected by strong copyright laws, regardless of rampant piracy in many countries in the Global South; however, as Al has increasingly become a major player, current copyright safeguards lose their primary roles in protecting cultural creators. As Yamamoto (2018, 1) points out, when computers emerged, the copyright issue was discussed, but it was obvious that “computers were not creators of cultural content but just tools with which creators could save labors, time, and money.” Al is more complicated than the computers, as Al technology itself backed by big data enables
computers to judge and decide what people tend to like, feel beautiful, or find funny.. . . The computers then are able to draw pictures, make music, or make stories that satisfy human being’s demand by combining expression elements according to people’s tendencies. Al works may contain intelligence equivalent to that in the works created by human beings.
Therefore, it is contested whether works made by Al could be protected by copyright (Yamamoto, 2018, 1).
Regarding this, Guadamuz (2017) points our that people are “in the throes of a technological revolution” that requires us to reconsider the interaction between computers and the creative process. For him,
that revolution is underpinned by the rapid development of machine learning software that produces autonomous systems that are capable of learning without being specifically programmed by a human. A computer program developed for machine learning purposes has a built-in algorithm that allows it to learn from data input, and to evolve and make future decisions.
Therefore, “when applied to art, music and literary works, machine learning algorithms are learning from input provided by programmers.” They learn from data to generate a new piece of work,
making independent decisions throughout the process to determine what the new work looks like. An important feature for this type of Al is that while programmers can set parameters, the work is generated by the computer program itself in a process akin to the thought processes of humans.
Al-supported cultural production could have very significant implications for copyright law, and how to understand and deal with this are some of the major issues at this particular juncture. Many nation-states, including Japan and Germany, currently protect works created by human intelligence and do not protect works created by machines. Humans still do actual production based on their expertise, regardless of the increasing role of Al in cultural production; therefore, these governments are not prepared for the upcoming years. However, the convergence of human beings and Al arguably needs to be emphasized, and governments have to develop new copyrights so that Al developers, digital platforms, and cultural creators are equally protected by new legal mechanisms.
Second, the proliferation of Al-driven cultural production in addition to distribution has facilitated the growth of large digital platforms, such as Google, Facebook, and Netflix. The period in which Al developed has been marked “by concentration and consolidation of ownership, not openness,” as in the cases of other digital technologies (Klinenberg and Benzecry, 2005, 10). Both Western and non-Western digital platforms, including OTT services, have acquired funding from venture capitals, focusing on Al-related technologies, or consolidated to become mega giants. As mainly discussed in Chapter 5, this trend has resulted in the lack of diversity of voice, which hurts our society’s cultural democracy. Therefore, it is critical to develop relevant policies for Al that guarantee diversity of cultural expressions. At the same time, it is also vital to “establish incentives for global platforms operating at the national level to contribute useful cultural data for decision making and draw up a national policy on data” (Kulesz, 2018b, 83). The concentration of media and culture in the hands of a few mega giants has not been new; however, in the Al age, the degree has become even steeper and faster than before, as platform giants and a few mega media moguls have enough capital, manpower, and know-how.
In this light, what the U.K. government decided gives us a good lesson. The U.K. government plans to create a technology regulator in 2020 to police platform giants such as Facebook and Google. The regulator, in particular nation-states, will be given powers to implement several new rules,
Al and cultural production 69 including an enforceable code of conduct for mega platform giants and greater data accessibility for platform users. The new move comes as several governments “unveil measures to protect citizens from privacy breaches and anti-competitive practices thrown up by the digital economy” in response to widespread calls for a curb on big tech’s power. Several governments, including the U.K. and Germany, are concerned that platform moguls like Google and Facebook are so large and have such all-embracing access to data that competitors or mid-sized platforms can no longer compete equally in the market (Murgia and Beioley, 2019).
The Department for Digital, Culture, Media and Sport of the U.K. said that digital services “must work for everyone, so that the incredible benefits of digital technologies are properly harnessed, consumers are protected and innovation thrives across the economy” (Murgia and Beioley, 2019). This implies that the technological disparities intensified by Al between the Global North and the Global South represent a noticeable challenge when it comes to fulfilling a balanced production and circulation of cultural content.
Third, while humans still play a pivotal role in the production of popular culture, it is desirable to ponder the lack of creativity and, again, diversity. As Guadamuz (2017) points out with the latest types of Al, “The computer program is no longer a tool; it actually makes many of the decisions involved in the creative process without human intervention.” Al might provide a tool to produce cultural content supported by big data and algorithms; however, the question is that its involvement in cultural production certainly implies a potential lack of diversity and creativity. As several cases in films and music have already proven, Al-driven cultural production emphasized what audiences liked and would like, instead of developing new forms of cultural content. Al, therefore, continues to create similar movies and music that people in the past enjoyed.
The encounters between Al and popular culture are mainly a contemporary phenomenon in that several cultural forms, such as films, music, webtoons, and digital games have rapidly expanded their use of Al in cultural production. In the study of the convergence of Al and popular culture, we must comprehend “what motivated the creation and adaption of Al for cultural production, what affordances the new products offer, and what ways consumers and producers make meaning through them” (Klinenberg and Benzecry, 2005, 16).
The questions of how people use [Al] for cultural work and what role these practices play in daily life are increasingly important to the study of creativity in action. So too are questions about the balance of power and control in cultural fields, which are dominated by a small number of commercial [platforms] whose reach extends nearly as far as the network itself. The emerging conflict between states, [platforms], and creative actors who aim to harness the power of [Al] in different ways promises to be one of the most important policy disputes of the 21st century.
(Klinenberg and Benzecry, 2005, 17)
Last, but not least, the rise of global platforms can be intensified because of the monopolistic possession of available data. As is well known, the lifeblood of the cultural system that Al is also rapidly becoming part of is data, and therefore, big data would be one of the key components of the digital economy and digital culture. One of major reasons for the success of large platforms is that they utilize data/metadata—representing a new type of cultural commodity—which can be reused. “In every country, the key to getting the most out of Al is having a ‘data-friendly ecosystem’ with unified standards and cross-platform sharing.” Al depends on data that can be analyzed in real time and brought to bear on concrete problems. Nations that promote open data sources and data sharing are the ones most likely to see Al advances. Having data that are “accessible for exploration” is a prerequisite for successful Al development (Barton et al., 2017; West, 2018, 23).
For example, by appropriating big data garnered from the users, they optimize recommendation algorithms for the users or sell them to advertisers. OTT platforms like Netflix and mega global media giants “are not simply online intermediaries; they are data companies and, as such, make every possible effort to safeguard and fully exploit their primary input” (Kulesz, 2018b, 81). Almost all forms of user interaction, including liking, friending, following, posting, retweeting, and commenting on digital platforms can be captured as data to turn into valuable commodities. Digital platforms turn these data into commodities, and this datafication process “endows platforms with the potential to develop techniques for predictive and real-time analytics, which are vital for delivering targeted advertising and services in a wide variety of economic sectors,” including the cultural sector (van Dijck et al., 2018, 33). Given the dominant position of global OTT platforms, local OTT platforms and cultural industries corporations have relatively less usable data than global mega giants, which puts them in jeopardy.
In the Al sector, those who make the most of Al as a technology are expectedly the big tech giants. The clearest examples of profitable, fruitful use of Al are Google, Amazon, Facebook, and Netflix (Faggella, 2019). These companies, as mega platform giants, have already had access to more data than any other company in the history of the world. Based on that they have built “a culture of connectivity and data,” and they have garnered huge profits. Equipped with Al, based on big data, and supported by algorithms, these digital platforms have produced, distributed, and expedited the global consumption of popular culture. For example,
Netflix’s recommendation engines are much more complex than most people assume they are, and are indeed, again, predicated on artificial intelligence on a digital platform in a virtual world where everything is potentially trackable. This allows Netflix to collect data that is extremely difficult to collect in the physical world.
When people use Netflix, their clicks, watches, pauses, and reviews are tracked by Netflix.
When popular culture encounters Al, several beneficial aspects happen; however, the convergence of these two seemingly separate areas does not guarantee the balanced growth of new digital culture and the digital economy, both nationally and globally. Regardless of its great mechanisms, Al, as with previous digital technologies, has become a major component of digital platforms and mega media corporations as they control big data and capital. In particular, the gap between the Global North and the Global South in this particular form of media convergence has deepened, as global platform giants control local cultural production based on their distribution power and networks. A handful of non-Western countries may advance their own Al and relevant digital technologies so that cultural creators can use them; however, the level of technology is not yet comparable to the Al developed in Western countries. When global OTT platforms and media giants control the flow of cultural content and big data, this divide will widen, not shrink. Al particularly depends on data, which is the driver behind machine learning. However, new and smaller digital platforms and media corporations in the Global South do not have access to as much data to develop powerful Al systems, with a few exceptions in China and Korea, which worsens the disparity between the Global North and the Global South.
The increasing role of Al may hurt democracy in culture and has brought about an Al divide. Several OTT platforms, as Al software developers and holders, will take over the entertainment industries, while traditional media firms centered on less-skilled digital technologies continuously lose their market shares, both nationally and globally. The customers also have no choice but to choose what OTT platforms recommend. Big data and algorithms that are major parts of Al are mostly owned by a few Westernbased platforms and mega media giants, and this systemic difference in the global context intensifies the global digital divide, which asks us to develop practical and reasonable standards to share data or distribute the benefits reasonably.