On the Social Influence of Social Media Algorithms:
(This text is a work in progress, as is the rest of this site and the project itself.)
Social media ranking algorithms have recently come under scrutiny for amplifying and encouraging sensational content. YouTube and other media providers have taken steps to address these concerns and attempt to limit some sensational content. But visibility, through search rankings and recommendation algorithms, is still dependent on engagement metrics: some combination of viewer attention measurements and interaction metrics. So the business model of promoting attention-grabbing videos continues to encourage sensationalism.
The practice of producing eyeball grabbing videos to distribute political disinformation is by now well known, but the visibility of non-political, yet sensational content is also amplified by engagement algorithms. These algorithms can impact cultural and political perceptions in more subtle ways — e.g., by amplifying stereotypes. At their most basic, the algorithms promote videos by seasoned “YouTubers” with the access to information and the inclination to strategize their work to maximize algorithmic appeal. Some types of videos and videomakers —- “crowd pleasers” —- get more visibility than others. This visibility bias can influence how the public perceives the social media world — and how they perceive other people in general –- a particular concern in politically contentious times rife with propaganda about an ever-expanding “other.” And, as Sophie Bishop points out, it can reinforce stereotypes by rewarding YouTubers for producing demographically stereotypical content and performing selves for the camera that algorithms favor.
As a result, videos by ordinary people are often seen by few or no human eyes. As with many contemporary human actions, robots may be the main audience: computer vision and artificial intelligence robots analyze social media posts, online videos, and faces of people looking at ads.
What the Robot Saw is a documentary by such a robot, using contrarian algorithms to make visible what algorithms normally hide. The Robot sees, and shows, what humans rarely get to see – but from the perspective and interests of a robot. Building on my own “Algocurator,” What the Robot Saw’s content is curated using algorithms that run counter to standard commercial ranking algorithms: it includes only videos with low view counts and channel subscriber counts. The real-time cinematography in What the Robot Saw is based on the imagined directorial style of the computer vision Robot, as it pans, zooms, segments, greyscales and edge-detects, looking for “features” in images to help it understand and organize the human world. Behind the scenes, computer vision and neural networks are used to eliminate undesirable clips and edit selected clips, then organize the clips into a stream-of-AI-“consciousness” linear structure, focusing on periodic “interviews” with human subjects framed as talking heads.
What the Robot Saw uses Amazon Rekognition, a popular commercial facial recognition service, to estimate age, gender, and mood as interpreted through facial expression. It then superimposes these characteristics at the bottom of human “interviewees'” video images, where viewers might expect to see the person’s name, occupation, and age. The labeling reveals the Robot’s inclination to define people in terms of the features Rekognition and similar surveillant services provide — features of great interest to marketers. As vloggers, job interviewees, students, and others, talk to the camera about whatever is on their minds, the Robot segments their face, stares into their eyes, and superimposes labels like “Ostensibly Calm Female, age 12-22.”) The absurd juxtaposition of complex human faces and first person narration with the Robot’s inane labels suggests the reductiveness of framing complex people according to characteristics determined useful to marketers. Have our identities as combinations of demographics, facial expressions, and other characteristics come to represent our essential selves more than do our names?
As the use of computer vision algorithms for analyzing faces has evolved in recent years, facial expression during ad viewing as a predictor of subsequent purchasing behavior has received intense interest from advertisers and researchers  . The implied holy grail of this research and resultant products is to distill the complexities of the human face into a tidy package: to translate the face into “buyer” or “not a buyer.” The Robot’s –- and Amazon Rekognition’s — obsessive interpretations of emotions using one word identifiers like “happy,” “calm”, “angry”, “disgusted,” “fearful”, etc. both simplify complex emotion and lack the social context within which humans typically consider expression and emotion. YouTubers focused on wide-eyed, upbeat performance may become “confused” or “surprised” in the marketing-centric mindset of the Robot. Likewise, the complexities of gender, ethnic, and cultural differences — in both general display of emotion and performance for YouTube — are awkwardly invisible to the Robot: a mischievous smirk, or simply the neutral expression referred to as “resting bitch face,” can become “disgust” in the eye of the Robot. (Amazon Rekognition has received criticism for its emotion detection features and its reading of female and darker-skinned faces in general, as well as for its sales to law enforcement agencies.)
In The Fourth Revolution – How the Infosphere is Reshaping Human Reality, Luciano Floridi writes of the selves performed for social media: “the micro-narratives we are producing and consuming are also changing our social selves and hence how we see ourselves. They represent an immense, externalized stream of consciousness…” In “What the Robot Saw,” the Robot consumes, magnifies, and reinterprets a nearly unseen portion of the collective, performed, YouTube micro-narrative – as only a robot would do.
On the Title:
“What the Robot Saw” is a wordplay on “What the Butler Saw,” an expression that refers to watching voyeuristically through a hypothetical or metaphorical keyhole.