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CHAPTER 5. DISCUSSION 26 algorithm as Agent to procure a professional license in its role of locating employment oppor- tunities for talents and to be regulated to protect the rights of talents? Another way talents interact with an agent is through a contract that specifies each person’s responsibilities and liabilities. Could we introduce contracts between YouTubers and the algorithm? YouTube content creators’ anthropomorphic language in regards to the algorithm demon- strates the various human-like roles that the algorithm plays but the algorithm still isn’t human. It can’t understand the nuances a human gatekeeper could. This indicates a socio- technical gap which is caused by automating tasks traditionally carried out by humans without careful consideration of the differences between humans and computers. Humans make flexible and subtly different decisions based on context while computers build rigid and simplified models by aggregating similar but distinct situations [3]. Humans can act upon ambiguity implicitly, while computers often solve problems in a black or white manner and require explicitness [1]. We reference Ju and Leifer’s implicit design framework, which aims to guide designers to consider the broad spectrum of interactions (from foreground to back- ground, and from reactive to proactive) and to encourage modelling the human-to-human interaction when designing for human-computer interactions [37]. One human-to-human interaction is asking questions to learn people’s intentions, asking for permission to do some- thing that may affect them, and challenging them if we disagree. What if, for example, a creator could ask the algorithm why their video got demonetized? Explainability is already a value that researchers have called for, but what exactly the shape of those explanations might be is an open question that algorithmic personas provide insight to. Engaging Users in Discussions about Fairness I embarked on this research not as an objective outsider but from the angle of redesigning the YouTube algorithm for fairness. This angle may have biased my questions and under- standings of the situation. For future directions, I plan to leverage participatory design to engage my research sub- jects. We will create more tools conceptually (e.g. personas) and physically (e.g. design provocations) to engage various stakeholders in algorithmic design. There have been calls for shared ownership and democratic governance1 of these large scale platforms [42, 61, 62]. This is not possible without deep engagement and co-design with the people who would be participating in that governance. I also plan to explore the implications for fairness. Definition of fairness vary depending on social, task and value contexts [43]. Viewing from the social context, YouTube content creators exemplifies a segment of population who are affected by the algorithmic decisions. Viewing from the value context, Lee et al. suggest that researchers should evaluate fairness of an algorithm not only based on its mathematical accuracy in distributing resources, they should also situate fairness among stakeholders’ preferences and personal values. Hobbyist YouTube content creators’ motivations go beyond simple monetary incentives. Algorithm 1The Platform Cooperativism conference was founded in 2017.PDF Image | How Content Creators Craft Algorithmic Personas and Perceive the Algorithm that Dictates their Work
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