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Update 21_1_Bias_Mitigation.ipynb

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Pietro Monticone
2023-11-24 11:18:20 +01:00
parent 362d8838e8
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Notebooks/Chap21/21_1_Bias_Mitigation.ipynb
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@@ -31,7 +31,7 @@
"source": [
"# **Notebook 21.1: Bias mitigation**\n",
"\n",
"This notebook investigates a post-processing method for bias mitigation (see figure 21.2 in the book). It based on this [blog](https://www.borealisai.com/research-blogs/tutorial1-bias-and-fairness-ai/) that I wrote for Borealis AI in 2019, which itself was derirved from [this blog](https://research.google.com/bigpicture/attacking-discrimination-in-ml/) by Wattenberg, Viégas, and Hardt.\n",
"This notebook investigates a post-processing method for bias mitigation (see figure 21.2 in the book). It based on this [blog](https://www.borealisai.com/research-blogs/tutorial1-bias-and-fairness-ai/) that I wrote for Borealis AI in 2019, which itself was derived from [this blog](https://research.google.com/bigpicture/attacking-discrimination-in-ml/) by Wattenberg, Viégas, and Hardt.\n",
"\n",
"Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
"\n",
@@ -172,7 +172,7 @@
"source": [
"# Blindness to protected attribute\n",
"\n",
"We'll first do the simplest possible thing. We'll choose the same threshold for both blue and yellow populations so that $\\tau_0$ = $\\tau_1$. Basically, we'll ingore what we know about the group membership. Let's see what the ramifications of that."
"We'll first do the simplest possible thing. We'll choose the same threshold for both blue and yellow populations so that $\\tau_0$ = $\\tau_1$. Basically, we'll ignore what we know about the group membership. Let's see what the ramifications of that."
],
"metadata": {
"id": "bE7yPyuWoSUy"
@@ -195,7 +195,7 @@
"source": [
"def compute_probability_get_loan(credit_scores, frequencies, threshold):\n",
" # TODO - Write this function\n",
" # Return the probability that somemone from this group loan based on the frequencies of each\n",
" # Return the probability that someone from this group loan based on the frequencies of each\n",
" # credit score for this group\n",
" # Replace this line:\n",
" prob = 0.5\n",
@@ -297,7 +297,7 @@
"\n",
"This criterion is clearly not great. The blue and yellow groups get given loans at different rates overall, and (for this threshold), the false alarms and true positives are also different, so it's not even fair when we consider whether the loans really were paid back. \n",
"\n",
"TODO -- investigate setting a different threshols $\\tau_{0}=\\tau_{1}$. Is it possible to make the overall rates that loans are given the same? Is it possible to make the false alarm rates the same? Is it possible to make the true positive rates the same?"
"TODO -- investigate setting a different threshold $\\tau_{0}=\\tau_{1}$. Is it possible to make the overall rates that loans are given the same? Is it possible to make the false alarm rates the same? Is it possible to make the true positive rates the same?"
],
"metadata": {
"id": "UCObTsa57uuC"