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CHAPTER 5. DISCUSSION 25 Algorithm as Gatekeeper Gatekeepers take many forms in the real world: job interviewers, college admissions officers, or a bouncer at a bar. Gatekeepers must make judgments and decisions about allocation of resources or opportunities. A gatekeeper is fraught with their own personal biases. The question of how much power do gatekeepers have? What are their decision making criteria? and how can those criteria be contested? are some of the open questions that also apply to the algorithm as Gatekeeper. Algorithm as Drug Dealer YouTube is a for profit company which mainly earns revenue through targeted ads on videos. As a company, YouTube wants to maximize time spent on the platform so viewers will click and watch ads, generating more ad revenue. It has been successful at doing so and is the second most visited website in the world with the highest average visit duration of 22 minutes [74]. At this scale there are public safety risks that we need to consider. In the disturbing live stream video that has since been removed from internet, the Christchurch mosque shooter invited viewers to subscribe to the most subscribed individual on YouTube. His attack was engineered for virality, and meant to feed the extremist content on YouTube and the internet [7]. YouTube has been publicly criticized for recommending toxic content, for the sake of engagement [9]. YouTube’s recommendation algorithm has also put minors at risk, recommending nightmarish knock-off versions of popular kid-friendly content [38], and enabling child predators to communicate with each other via the comments section [56]. Conversations & Contracts: Design and Policy Implications Analyzing the closest human equivalents of algorithmic personas is an effective conceptual tool in think about the design and policy implications of algorithms as they are used in the real world. After decades of labor struggles many states require talent agents to procure professional licenses and regulate their business practices through legislature. For example, California’s Talent Agency Act “prohibits agents from giving their clients false or misleading information concerning employment engagements; sending them to unsafe places.” [89] There are cases where lawsuits were brought against talent agents in manipulating talents into accepting jobs for the sole benefits of the agent. In our research, we have learned that YouTubers, under the manipulation of the algorithm, modify their behaviors to “fit” themselves through the win- dow of opportunity that the algorithm provides, sometimes compromising their own creative integrity and even personal safety (in the case of harassment). Here we need to respect the multi-faceted motivations of YouTube content creators including monetary, personal passion, and altruistic causes. Legal measures in the offline world that are intended to protect the talents’ interests are lacking in the world where the algorithm acts as an Agent. What would it look like for anPDF Image | How Content Creators Craft Algorithmic Personas and Perceive the Algorithm that Dictates their Work
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