Unanswered Questions about YouTube: Shorts Impact, View Count, and Gender Bias

📹Shorts, the vertical videos under 60 seconds, may be cannibalizing long-form video views on YouTube.

🕰️The definition of a view on YouTube shorts is unclear, potentially allowing for manipulated view counts.

👩‍💻There seems to be a gender bias in the YouTube algorithm, favoring male creators and limiting visibility for female creators.

🧪AI algorithms, like YouTube's, can have biases if trained on biased data sets, exacerbating existing inequalities.

⏱️Analyzing YouTube data reveals a significant disparity in views between male and female creators.

Is YouTube shorts negatively impacting long-form content?

—The introduction of YouTube shorts, vertical videos under 60 seconds, may be cannibalizing long-form video views on the platform.

How are YouTube shorts views counted?

—The duration for a view on YouTube shorts is undefined, making it difficult to accurately gauge engagement and potentially allowing for manipulated view counts.

Are there gender biases in YouTube's algorithms?

—There is evidence of a gender bias favoring male creators in the YouTube algorithm, which may limit visibility for female creators.

How can AI algorithms exhibit biases?

—AI algorithms, such as YouTube's, can exhibit biases if trained on biased data sets, amplifying existing inequalities.

Is there a disparity in views between male and female creators?

—Analyzing YouTube data reveals a significant difference in views between male and female creators, indicating potential bias in the platform's algorithms.

00:00The video aims to address unanswered questions about YouTube.

04:07YouTube shorts, vertical videos under 60 seconds, may affect long-form content views on the platform.

09:28The definition of a view on YouTube shorts is unclear, potentially allowing for manipulated view counts.

13:40There seems to be a gender bias in the YouTube algorithm, favoring male creators and limiting visibility for female creators.

18:35AI algorithms, like YouTube's, can have biases if trained on biased data sets, exacerbating existing inequalities.