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Disempowering empowerment of users in the Al age
While the two different models—namely, the Facebook model and the Netflix model—in digital platforms supported by Al, have differentiated their business strategies in utilizing users, Al has especially played contrasting and opposing roles in influencing the users: through both empowerment and disempowerment. Like other digital technologies, Al and digital platforms have developed a dual role, proving their ambivalent perspectives.
To begin with, empowerment implies that Al as a new cutting-edge digital technology would help users at the workplace. By implementing Al technology to handle everything from complex predictions to everyday tasks, creators and cultural companies find that employees are better able to focus on higher-level work that can result in greater efficiency, more creativity, and stronger business return on investment (Butler, 2019). Regardless of some concerns, others also believe that Al helps people do their jobs better at the workplace. Al technology and digital platforms empower audiences with more control (Asia-Pacific Broadcasting, 2018). With Al technology’s shift from the lab to the mainstream in the last decade, workforces from nearly every industry have expressed concern about the future of their positions. There is a misconception that Al will replace human creativity and ingenuity, turning content creation, distribution, and marketing into vanilla, machine-driven tasks. There are several ways that Al may actually help empower the entertainment workforce (Butler, 2019): 1) allow for more creativity and strategic thinking, 2) provide intelligence on the content that will drive the strongest results, 3) create new growth and innovative business opportunities, 4) enable new and unique experiences for audiences, and 5) turn niche interests into viable markets for content. Al and digital platforms enable audiences to present their power, which means that they allow viewers “to customize their own selections and content preferences” (Asia-Pacific Broadcasting, 2018).
According to this perspective, which emphasizes the optimistic aspects of Al, Al does not limit the range and diversity of content.
In reality, the opposite is true. Al can help new and diverse voices find audiences. It can also transform niche content genres into viable markets and give rise to a new generation of creators who are reflective of society’s diverse interests and tastes.
We have only begun to understand Al’s applications for entertainment and how technologies can empower the industry’s workforce. Those who embrace how Al can help, inform, and enable a new era of entertainment will be in the best position to thrive as Al evolves further. Popular social platforms like YouTube, Instagram, and Twitch have brought content to viewers that TV was not paying attention to or had written off completely, such as gamers (Butler, 2019). In other words, Al “can help to empower numerous creators, make the cultural industries more efficient and increase the number of artworks, which is in the interest of the public” (Kulesz, 2018a, 2). As Vincent (2019a) points out,
Machine learning is particularly well-suited for this sort of task because it can spot patterns in a big pot of data. More importantly, the “consumption patterns” his company studies are always evolving. What people watch and how they do it has changed hugely in the last few years. It’s better to have a system built with machine learning that can adapt to these shifts, rather than a hard-coded algorithm that has to be updated manually.
In other words,
The economies of data mining redefine relations of power, not merely by selling user attention but by tapping into “the everyday life” of users and refashioning it from within, guided by commercial norms such as the presumed value to advertisers.
(Langlois and Elmer, 2013, 4)
However, there are also a number of caveats that go hand in hand with these advantages. Some of the most important challenges are the potential of Al to create bias in popular culture that “users are exposed to, the necessity to collect and store extensive data on all users, the risks of targeted manipulation, and the limited agency users experience while interacting with Al-driven tools” (Helberger et al., 2019, 11). This perspective clearly claims that Al disempowers users. As van Dijck et al. (2018, 33) point out, while datafication is able to be understood as a techno-commercial strategy deployed by platform owners, it can concurrently be regarded as a user practice. Digital platforms systematically collect and analyze user data. At the same time, they circulate these data through application programming interfaces to third parties and through user interfaces to end users, enabling them to trace the activities of friends and colleagues, keep track of public events, and participate in the online economy. These scholars also emphasize the mechanism of the commodification of user data. For them, “commodification is intensified by mechanisms of datafication as the massive amount of user data collected and processed by online platforms provide insight into users’ interests, preferences, and needs at particular moments in time” (van Dijck et al., 2018, 37).
Commodification mechanisms are “empowering and disempowering to users” (van Dijck et al., 2018, 37). They are empowering because customers have power to decide what they want, and OTT platforms must meet the audiences’ needs.
The traditional TV schedule is becoming less and less relevant for many demographic groups and with rhe upcoming launch of major new OTT services ... the pressure on the traditional TV model will increase. OTT enables choice, flexibility and better value for consumers, we should expect the growth of these services to continue to accelerate.
At the same time, it is disempowering platform users, because various platforms exploit cultural labor and the immaterial labor of users, while actualizing the precarization of platform service workers. As van Doorn (2017, 904) aptly puts it, it should be stressed that the structural degradation of platform work
is a central strategy for valorizing the tension between its indispensability and expendability, as it allows companies to keep hiring rates high and labor prices low, thus optimizing the exploitation of precarious workers looking to supplement their wages in order to make ends meet.
Furthermore, these mechanisms “lead to a concentration of economic power in rhe hands of a few platform owners and operators, because they strategically position themselves as aggregators and gatekeeping mediators” (Fuchs, 2014; van Dijck et al., 2018, 37). Concerns about the impact of Al on the cultural sector are increasing.
More specifically, several considerations indicate that Al disempowers users. This is particularly important, as Al-driven recommendation technology can create considerable gatekeeping power regarding the information diets of users. The gatekeeping power of platforms, such as Google, Facebook, and Netflix, exceeds the ability to define the priorities of feeds. These platforms also increasingly use Al-driven tools to filter specific types of content (Helberger et al., 2019). Al-driven tools reshape the way consumers enjoy cultural content. Digitization and datafication also affect the overall structure and diversity of the cultural landscape. There are new players such as social media platforms and OTT service platforms arriving on the scene and affecting the process of producing, distributing, and exhibiting culture. An immediate side-effect on the role of culture in society is the disaggregation and unbundling of cultural products but also the amplification of mis- and disinformation and the potential for malicious manipulations of popular culture. The reliance on big data also creates a new economy of scale, in
Personalization of culture in the Al era 105 which those players with the most access to data are better able to provide personalized news to users (Stone, 2014; Helberger et al., 2019).
Unlike in the traditional media and cultural industries, which depart from the idea of a sender transmitting culture and information to an unidentified mass audience, one important implication of the use of Al-driven tools in the cultural sector is that audiences can be targeted in terms of far more precise groups, or even on an individual level.
Automated filtering and sorting mechanisms can affect the exercise of an individual’s right to receive culture and information based on personal characteristics and preferences. The use of Al-driven tools must not result in a situation in which certain parts of the population or users with particular characteristics are structurally excluded from accessing information, or where society experiences new digital divides.
(Helberger et al., 2019, 28)
In the Al-driven digital environment, “the audience is more than an anonymous mass of receivers.” Users have “a greater influence over the dissemination of online information” and culture. Therefore, it is critical for individual members to take a more active role in the process of producing and distributing culture (Helberger et al., 2019, 29). In the Al era, what we have to focus on is the impact on the most disempowered and marginalized people in our society (Bacciarelli, 2019).
Al algorithms have become the alchemy of our digital age, “the search for a magical, mystical process” that guides people to “turn a pile of data into gold. They are widely seen as a silver bullet that can generate new insights and expert decisions on an unprecedented speed and scale” (Taylor, 2019). Al can be categorized as
a source of opportunity for oppressed and marginalized people, with tremendous focus put on closing the hardware, software, and access gaps on the Internet for various communities. Among the most prevalent ideas about the political aspects of technology disenfranchisement and opportunity are theories that center on the concept of the digital divide.
(Noble, 2018, 160)
Digital divide narratives have focused on a few primary dimensions of disempowerment that have led to technological deficits between access to computer, development of skills and training in IT technologies, and internet connectivity (Wilhelm, 2006; Noble, 2018).
It is not uncommon to witness that new digital technologies have advanced both empowerment and disempowerment. However, in the case of Al, it is crucial to understand these opposing aspects carefully because the general public, for the most part, cannot understand this particular digital technology. Again, Al can empower and disempower users, both cultural creators and consumers. Regardless of its promise, Al has brought about several negative aspects, as it disempowers users. Al is evolving, and some of the problems will be resolved. However, as discussed, Al itself adds several socio-economic and cultural problems to contemporary society. Instead of helping users, this new digital technology has continued to function negatively. The disempowerment of users has been increasing, and Al is also connected with representational harms.