JCU Communications Department Hosts Talk on Algorithmic Resistance Project
The JCU Communications Department organized a talk called “The Algorithmic Resistance Project,” on March 23, 2022, in the context of the Digital Delights and Disturbances Lecture Series.
The speakers were Professor Tiziano Bonini from the University of Siena and Professor Emiliano Treré from Cardiff University. Bonini’s work is focused on the political economy of the media. Treré’s research focuses on digital activism and social movements in critical data studies, with an emphasis on Latin America and the global south.
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Treré began by explaining his and Bonini’s roles in co-founding the Algorithmic Resistance project, a study that focuses on the behaviors of delivery service drivers. He said that “Bonini specializes in cultural studies,” including ratio studies and studies of audiences, whereas he focuses on politics and digital activism as a form of protest. Though they have different research interests, Treré says they both recognize the “shift of the true algorithmic power” that is currently increasing. They joined forces and decided to launch the project centered on advocating for digital equity.
Algorithms increasingly shape our lives across a variety of social domains, including politics, culture, financial systems, and labor. Treré said that algorithmic resistance is a term he coined when he worked as an associate professor in Mexico. In this role, he observed how bots and non-human actors were “reshaping the political scenario of communication in Mexico.” He described these as “dirty practices” by the government and parties in Mexico, which led him to focus on the resistance that emerged to prevent them.
Similarly, Bonini also witnessed “dirty practices” in the realm of culture in the same digital spaces. With this project, Bonini wanted to bring attention to these practices and to the tactics that people developed to live with algorithmic institutions of power. This research project originated from the shared frustration that researchers were seeing in algorithmic governance frameworks, which Treré described as “bleak.” To counteract this, he said they wanted to illuminate the ways in which people can make sense of and repurpose algorithmic powers.
Treré and Bonini developed two ways to “map the terrain” of algorithmic governance. The first is to resist algorithms, which entails activists fighting against the discrimination and biases associated with algorithmic organizations. The second is the way to work and operate through algorithms on various platforms. Treré emphasized that these approaches are interconnected and operate together through algorithms on platforms.
To mark the scope of these two approaches, Treré and Bonini developed a methodological approach by interviewing food delivery riders across Italy, Mexico, China, India, and Spain. Treré and Bonini followed the participants for a year and closely observed the drivers’ private online groups via WhatsApp, WeChat, and Facebook. There was also physical observation of the participants during their delivery shifts.
Bonini then shared their findings. He explained that methodology was one of their main challenges in trying to ground their findings and theories on a “strong empirical ground.” Thus, they adopted a Gandhi Theory approach, which aims to provide equity or justice through promoting technology that is appropriate to basic needs (clothing, food, health). They did so by working in a cyclical and iterative process of figuring out the first research questions and then returning to the transcripts of the interviews. From these interviews, Bonini shared that there were three main clusters that outlined their findings: competitive features of food delivery platforms (gamification), riders’ individual and collective gaming tactics, and the emergence of solidarity networks through private online chat groups.
The first cluster, competitive features of food delivery platforms, is categorized by the term “gamification.” This means that the platforms design the apps in a way that forces the drivers to work against one another through ranking systems. The ranking system pressures the participants to perform better to receive benefits in return, which represents what sociologists call the “Matthew Effect.” Bonini explained the Matthew Effect as “the rich get richer, and the poor get poorer.” This implication led to the discovery of four resistance tactics adopted specifically by Chinese riders called “Shuadan”- creating fake orders and refusing to accept ones from certain regions.
Bonini further explained Shuadan as being a result of riders understanding that they must obtain more orders to reap the benefits of the ranking system. The riders do this by using two or more mobile phones to act as the customer and delivery rider at the same time. In doing so, the rider can increase the number of their orders, achieve the targets they want, and obtain the corresponding bonus.
The researchers concluded with three main ideas from their observation: learning environments, hidden transcripts of resistance, and solidarity-building spaces. In learning environments, newcomers can learn from more experienced riders and exchange tips. Here, Bonini said that riders “learn from their peers and build a collective algorithmic imagery.” The discovery of hidden transcripts of resistance refers to the private chats that riders organize in fear of being punished by the corporate administrators of WeChat groups. An example of solidarity-building spaces are private groups that allow workers to bond with their peers, and since online food delivery is an individual role, a sense of community can still be built. Bonini said this environment is favorable for the bonds of “solidarity, exchanges of information, and increased awareness of one’s condition of subalternity or exploitation.”
Treré concluded the talk by explaining how their conclusions led to two different definitions of the term “solidarity.” The first is “solidarity mediated by algorithms,” which implies that many food delivery riders find opportunities to gather together, thanks to other types of algorithms that lead them to like-minded people such as on TikTok or YouTube. The second definition is “solidarity around algorithms,” in which riders can exercise “individual and collective agency to improve their working conditions, both partially and temporarily,” despite the lack of opportunities for collective action. Treré and Bonini said that given the severe power imbalance and injustices that define the messaging platform society, forms of resistance and solidarity are emerging every day in all work environments to counteract it.
