Software as A Control Mechanism
Elif Geçyatan
I updated my Instagram app last month and I noticed a new feature Facebook had brought to the platform: user created augmented reality (AR) filters for My Story. AR is a system that can add virtual objects to the real world, which seem to coexist in the same time and space. (Bimber & Raskar, 2005). Many original Instagram filters like heart eyes and flower crown are based on this technology. Yet these new filters were different. As I scrolled left on my stories, I started to see a new world– one that offered more than dog face filters with long tongues. There were futuristic, abstract face filters that made one’s face glossy and metallic, almost like a cyborg. Some filters created typical Turkish scenes like a wedding or a coffeehouse. Last but definitely not least were slot machine-like personality quizzes that showed “what type of x you are” . These were some of the new filter trends that I had encountered, and which people seemed to enjoy. However, as one tries to find a specific filter to try themselves, they can quickly realize searching for filters is not as easy as it appears.
Before continuing with the possible reasons and outcomes of seemingly amateurishly design I would like to briefly explain how this technology works and where it originated. One of the primary historical examples of AR systems is a U.S Air Force’s Armstrong Laboratory system called Virtual Fixture, developed by Louis Rosenberg in 1992; the system was meant to create simulation-like experiences for soldiers with the help of body attachments. Currently, the use of this type of technology has expanded to every possible industry, from entertainment or gaming to education and health. Its applications are broad because AR can blend readily into pretty much any digital and real world. With AR, screens and phones are turned into gates between two worlds. When we look to social media, is Snapchat (Picaboo as its initial name) offers one early example of AR. Much of the technology Snapchat uses was created by a startup called Looksery, from Ukraine, that specialized in face recognition and modification technologies for real–time video. Snapchat paid $150 million for the startup and integrated the technology to their platform (Krasnikov,2015). Meanwhile, Facebook felt the need to catch up with this new trend, and they introduced their AR tool, Spark AR (which was called Camera Effects at the time), to Instagram in mid-2017. Yet Snapchat still was the first to launch a desktop app for users to create their own filters; based on high demand, they offered a follow-up version last year called Lens Studio 2.0. Start of the new decade Facebook tries to encourage businesses to create their own brand theme filters on Instagram via Spark AR (Indestry, 2019).
With the Spark AR software one can detect a face, track a hand and allow a filter to interact with three main expressions – smiling, kissing and surprised. But one can also add virtual objects in their surroundings. While I was doing this research, I wanted to try it out myself. With a simple YouTube tutorial, and without any technical expertise, I was able to create a glowing filter. I was expecting to have a more sense of how AR worked, but after tinkering with the software and its interface felt like a glorified version of Photoshop and Rhino. As a user I still had little sense of how it functioned. It just felt “cool” and “sciency”.
Interface of Spark AR and its features do not provide a holistic view of the ways and means of its Artificial Intelligence (AI). Lay users have little sense of how the AI works, or what these products can do besides provide entertainment Filters are equally inscrutable in Instagram. Even though individuals can produce their own filters, it is difficult for that filter to reach the masses because the ability to search for filters is absent. One can either search for an Instagram user that they know who creates filters, or by seeing a filter on your story and saving it. After review if filters were rejected creators received a feed-back why it could not make the cut. I am currently waiting for mine.
Taking all into consideration, politics of the Instagram Filters comes into the picture. Facebook actively limits users’ reach inside of the system. Spark AR makes the program seem like magic. Because of limited tools given to creators, the outcome of their work also has been constrained. This limitation takes away the chances to criticize the platform and its culture . One of the examples is “plastic surgery” filter by Daniel Mooney, which showed the dark side of the process and tried to explain how individuals do not need to be “fixed”. Yet, Instagram decided to take it down because it believed to have “negative effect on users’ mental health”(BBC, 2019). However, many individuals and users continue to seek surgery because of selfie dysmorphia (wanting to look like individual’s selfies) (Hunt, 2019). Then the questions arises, “Is banning a sustainable solution for preventing negative effects on mental health or is it an easy coverup?” Not being able to search filters gives all the power to the algorithm and the platform itself. Recommended and displayed filters are up to Facebook, rather than users. Moreover, Facebook’s invitations for businesses to participate this culture allows them to influence without waiting for approval. This way business and add related filters will dominate the platform. Meanwhile, Facebook’s monopoly continues to grow and to shape our trends — but maybe the most important thing for users to decide what is right to be shared what isn’t.
Reference:
Bimber, O., & Raskar, R. (2005). Spatial augmented reality: merging real and virtual worlds. AK Peters/CRC Press.
Hunt, E. (2019, January 23). Faking it: how selfie dysmorphia is driving people to seek surgery. The Guardian. Retrieved from https://www.theguardian.com/lifeandstyle/2019/jan/23/faking-it-how-selfie-dysmorphia-is-driving-people-to-seek-surgery
Rosenberg, L.B. (1993). Virtual fixtures: Perceptual tools for telerobotic manipulation. Proceedings of IEEE Virtual Reality Annual International Symposium, 76-82.
Maad, S. (Ed.). (2010). Augmented reality. BoD–Books on Demand.
Krasnikov, D. (2015, December 4). Snapchat’s $150 million acquisition of Looksery stands as Ukrainian record. Kyiv Post. Retrieved from https://www.kyivpost.com/article/content/ukraines-it-edge/snapchats-150-million-acquisition-of-looksery-stands-as-ukrainian-record-403470.html
Indestry (2019, October 11). Retrieved from https://www.indestry.com/blog/2019/10/11/the-brief-history-of-social-media-ar-filters