Shared August 9, 2019
As Google prevented Tulsi Gabbard from running campaign ads on Google Search after her successful debate night, the reliability and credibility of moderation driven by machine-learning algorithms is put to question.
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On the night after the first Democratic debates, Tulsi Gabbard was the single most Google-searched candidate by far. However, Google suspended her advertising account for 6 hours and so drastically limited her ability to direct newcomers to her campaign website.
The classic argument goes that machine-learning algorithms are mathematical, and by their very nature, neutral and unbiased. In reality, algorithms reinforce biases they learn about from their training data.
These are the biases identified in the video that can negatively impact outcomes of machine-learning algorithms:
1. a) Incorporated biases from the training data
1. b) Bad training data
2. User-manipulated bias
3. Manual interference
4. Marginalizing minorities
5. Corporate (for profit/influence) bias
 https://www.theguardian.com/technolog... ; b) https://www.washingtontimes.com/news/...
 https://www.statista.com/statistics/2... b) https://www.omnicoreagency.com/facebo...
Music by CO.AG Music https://www.youtube.com/channel/UCcav...
The footage and images featured in the video were for critical analysis, commentary and parody, which are protected under the Fair Use laws of the United States Copyright act of 1976.
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