ch3 recommendations

03_ethics.ipynb

@@ -196,7 +196,7 @@
    "source": [
     "Of course, the project managers and engineers and technicians involved were just living their ordinary lives. Caring for their families, going to the church on Sunday, doing their jobs as best as they could. Following orders. The marketers were just doing what they could to meet their business development goals. Edwin Black, author of \"IBM and the Holocaust\", said: \"To the blind technocrat, the means were more important than the ends. The destruction of the Jewish people became even less important because the invigorating nature of IBM's technical achievement was only heightened by the fantastical profits to be made at a time when bread lines stretched across the world.\"\n",
     "\n",
-    "Step back for a moment and consider: how would you feel if you discovered that you had been part of a system that ending up hurting society? Would you even know? Would you be open to finding out? How can you help make sure this doesn't happen? We have described the most extreme situation here in Nazi Germany, but there are many negative societal consequences happening due to AI and machine learning right now, some of which we'll describe in this chapter.\n",
+    "Step back for a moment and consider: how would you feel if you discovered that you had been part of a system that ended up hurting society? Would you even know? Would you be open to finding out? How can you help make sure this doesn't happen? We have described the most extreme situation here in Nazi Germany, but there are many negative societal consequences happening due to AI and machine learning right now, some of which we'll describe in this chapter.\n",
     "\n",
     "It's not just a moral burden either. Sometimes, technologists pay very directly for their actions. For instance, the first person who was jailed as a result of the Volkswagen scandal, where the car company cheated on their diesel emissions tests, was not the manager that oversaw the project, or an executive at the helm of the company. It was one of the engineers, James Liang, who just did what he was told.\n",
     "\n",
@@ -204,7 +204,7 @@
     "\n",
     "Okay, so hopefully we have convinced you that you ought to care. But what should you do? As data scientists, we're naturally inclined to focus on making our model better at optimizing some metric. But optimizing that metric may not actually lead to better outcomes. And even if optimizing that metric *does* help create better outcomes, it almost certainly won't be the only thing that matters. Consider the pipeline of steps that occurs between the development of a model or an algorithm by a researcher or practitioner, and the point at which this work is actually used to make some decision. This entire pipeline needs to be considered *as a whole* if we're to have a hope of getting the kinds of outcomes we want.\n",
     "\n",
-    "Normally there is a very long chain from one end to the other. This is especially true if you are a researcher where you don't even know if your research will ever get used for anything, or if you're involved in data collection, which is even earlier in the pipeline. But no-one is better placed to inform everyone involved in this chain about the capabilities, constraints, and details of your work than you are. Although there's no \"silver bullet\" that can ensure your work is used the right way, by getting involved in the process, and asking the right questions, you can at the very least ensured that the right issues are being considered.\n",
+    "Normally there is a very long chain from one end to the other. This is especially true if you are a researcher where you don't even know if your research will ever get used for anything, or if you're involved in data collection, which is even earlier in the pipeline. But no-one is better placed to inform everyone involved in this chain about the capabilities, constraints, and details of your work than you are. Although there's no \"silver bullet\" that can ensure your work is used the right way, by getting involved in the process, and asking the right questions, you can at the very least ensure that the right issues are being considered.\n",
     "\n",
     "Sometimes, the right response to being asked to do a piece of work is to just say \"no\". Often, however, the response we hear is \"if I don’t do it, someone else will\". But consider this: if you’ve been picked for the job, you’re the best person they’ve found; so if you don’t do it, the best person isn’t working on that project. If the first 5 they ask all say no too, then even better!"
    ]
@@ -700,7 +700,7 @@
     "- support good policy\n",
     "- increase diversity\n",
     "\n",
-    "Let's walk through each step next, staring with analyzing a project you are working on."
+    "Let's walk through each step next, starting with analyzing a project you are working on."
    ]
   },
   {
@@ -726,7 +726,7 @@
     "\n",
     "These questions may be able to help you identify outstanding issues, and possible alternatives that are easier to understand and control. In addition to asking the right questions, it's also important to consider practices and processes to implement.\n",
     "\n",
-    "One thing to consider at this stage is what data you are collecting and storing. Data often ends up being used for different purposes than why it was originally collected.  For instance, IBM began selling to Nazi Germany well before the Holocaust, including helping with Germany’s 1933 census conducted by Adolf Hitler, which was effective at identifying far more Jewish people than had previously been recognized in Germany. US census data was used to round up Japanese-Americans (who were US citizens) for internment during World War II. It is important to recognize how data and images collected can be weaponized later. Columbia professor [Tim Wu wrote](https://www.nytimes.com/2019/04/10/opinion/sunday/privacy-capitalism.html) that “You must assume that any personal data that Facebook or Android keeps are data that governments around the world will try to get or that thieves will try to steal.”"
+    "One thing to consider at this stage is what data you are collecting and storing. Data often ends up being used for different purposes than why it was originally collected for.  For instance, IBM began selling to Nazi Germany well before the Holocaust, including helping with Germany’s 1933 census conducted by Adolf Hitler, which was effective at identifying far more Jewish people than had previously been recognized in Germany. US census data was used to round up Japanese-Americans (who were US citizens) for internment during World War II. It is important to recognize how data and images collected can be weaponized later. Columbia professor [Tim Wu wrote](https://www.nytimes.com/2019/04/10/opinion/sunday/privacy-capitalism.html) that “You must assume that any personal data that Facebook or Android keeps are data that governments around the world will try to get or that thieves will try to steal.”"
    ]
   },
   {
@@ -1020,7 +1020,7 @@
     "- Run the first notebook yourself\n",
     "- Uploaded an image that you find in the first notebook; then try a few different images of different kinds to see what happens\n",
     "- Run the second notebook, collecting your own dataset based on image search queries that you come up with\n",
-    "- Thought about how you can use deep learning to help you with your own projects, including what kinds of data you could use, what kinds of problems may come up, and how you might be able to mitigate these issues in practice.\n",
+    "- Think about how you can use deep learning to help you with your own projects, including what kinds of data you could use, what kinds of problems may come up, and how you might be able to mitigate these issues in practice.\n",
     "\n",
     "In the next section of the book we will learn about how and why deep learning works, instead of just seeing how we can use it in practice. Understanding the how and why is important for both practitioners and researchers, because in this fairly new field nearly every project requires some level of customisation and debugging. The better you understand the foundations of deep learning, the better your models will be. These foundations are less important for executives, product managers, and so forth (although still useful, so feel free to keep reading!), but they are critical for anybody who is actually training and deploying models themselves."
    ]
@@ -1038,6 +1038,18 @@
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