Merge pull request #4736 from lamberta/ml-fairness-note

Add note about ML fairness

samples/core/tutorials/keras/basic_regression.ipynb

@@ -5,6 +5,8 @@
     "colab": {
       "name": "basic-regression.ipynb",
       "version": "0.3.2",
+      "views": {},
+      "default_view": {},
       "provenance": [],
       "private_outputs": true,
       "collapsed_sections": [
@@ -32,7 +34,12 @@
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         "id": "AwOEIRJC6Une",
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@@ -55,7 +62,12 @@
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         "id": "KyPEtTqk6VdG",
         "colab_type": "code",
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+        "colab": {
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@@ -126,7 +138,12 @@
       "metadata": {
         "id": "1rRo8oNqZ-Rj",
         "colab_type": "code",
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+        "colab": {
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@@ -156,7 +173,12 @@
       "metadata": {
         "id": "p9kxxgzvzlyz",
         "colab_type": "code",
-        "colab": {}
+        "colab": {
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+          }
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       "cell_type": "code",
       "source": [
@@ -188,7 +210,12 @@
       "metadata": {
         "id": "Ujqcgkipr65P",
         "colab_type": "code",
-        "colab": {}
+        "colab": {
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@@ -221,14 +248,21 @@
         "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",
         "\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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         "colab_type": "code",
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+        "colab": {
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@@ -251,7 +285,12 @@
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@@ -282,7 +321,12 @@
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@@ -307,7 +351,12 @@
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@@ -349,7 +398,12 @@
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@@ -390,7 +444,12 @@
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@@ -424,7 +483,12 @@
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@@ -463,7 +527,12 @@
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@@ -497,7 +566,12 @@
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@@ -524,7 +598,12 @@
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@@ -554,4 +633,4 @@
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-}
+}
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