  With YouTube's growing importance as a news platform, its recommendation
system came under increased scrutiny. Recognizing YouTube's recommendation
system as a broadcaster of media, we explore the applicability of laws that
require broadcasters to give important political, ideological, and social
groups adequate opportunity to express themselves in the broadcasted program of
the service. We present audits as an important tool to enforce such laws and to
ensure that a system operates in the public's interest. To examine whether
YouTube is enacting certain biases, we collected video recommendations about
political topics by following chains of ten recommendations per video. Our
findings suggest that YouTube's recommendation system is enacting important
biases. We find that YouTube is recommending increasingly popular but topically
unrelated videos. The sadness evoked by the recommended videos decreases while
the happiness increases. We discuss the strong popularity bias we identified
and analyze the link between the popularity of content and emotions. We also
discuss how audits empower researchers and civic hackers to monitor complex
machine learning (ML)-based systems like YouTube's recommendation system.
