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Partnering with researchers at UC Berkeley to improve the use of ML
Today, the consequences of exposing algorithmic decisions and machine learning models to hundreds of millions of people are poorly understood. Even less is known about how these algorithms might interact with social dynamics: people might change their behaviour in response to what the algorithms recommend to them, and as a result of this shift in behaviour the algorithm itself might change, creating a potentially self-reinforcing feedback loop. We also know that individuals or groups will seek to game or exploit our algorithms and safeguarding against this is essential.
artificial  intelligence  ai  machine  learning  thoughts  though  twitter  unintended  consequence  consequences  behavior  behaviors  behavioral  change  changes  musing  deep  share  sharing  publishing  disseminating  research 
january 2019 by yencarnacion
Flexible Sampling general guidance - Search Console Help
"There are two types of sampling we advise: metering, which provides users with a quota of free articles to consume, after which paywalls will start appearing; and lead-in, which offers a portion of an article’s content without it being shown in full."
monetization  free  content  guidance  advise  google  publish  publishing 
january 2019 by yencarnacion

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