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Unverified Commit 37ba2304 authored by Mark Daoust's avatar Mark Daoust Committed by GitHub
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Merge pull request #4736 from lamberta/ml-fairness-note 

Add note about ML fairness
parents e7a055d1 dc2de083
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samples/core/tutorials/keras/basic_regression.ipynb
View file @ 37ba2304
...@@ -5,6 +5,8 @@ ...@@ -5,6 +5,8 @@
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...@@ -32,7 +34,12 @@ ...@@ -32,7 +34,12 @@
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...@@ -221,14 +248,21 @@ ...@@ -221,14 +248,21 @@
"12. 1000 * (Bk - 0.63) ** 2 where Bk is the proportion of Black people by town.\n", "12. 1000 * (Bk - 0.63) ** 2 where Bk is the proportion of Black people by town.\n",
"13. Percentage lower status of the population.\n", "13. Percentage lower status of the population.\n",
"\n", "\n",
"Each one of these input data features is stored using a different scale. Some feature are represented by a proportion between 0 and 1, other features are ranges between 1 and 12, some are ranges between 0 and 100, and so on. This is often the case with real-world data, and understanding how to explore and clean such data is an important skill to develop." "Each one of these input data features is stored using a different scale. Some feature are represented by a proportion between 0 and 1, other features are ranges between 1 and 12, some are ranges between 0 and 100, and so on. This is often the case with real-world data, and understanding how to explore and clean such data is an important skill to develop.\n",
"\n",
"Key Point: As a modeler and developer, think about how this data is used and the potential benefits and harm a model's predictions can cause. A model like this could reinforce societal biases and disparities. Is a feature relevant to the problem you want to solve or will it introduce bias? For more information, read about [ML fairness](https://developers.google.com/machine-learning/fairness-overview/)."
] ]
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} }
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