Updates

03_ethics.ipynb

@@ -82,9 +82,9 @@
    "source": [
     "We are going to start with three specific examples that illustrate three common ethical issues in tech:\n",
     "\n",
-    "1.  *Recourse processes*--Arkansas's buggy healthcare algorithms left patients stranded.\n",
-    "2.  *Feedback loops*--YouTube's recommendation system helped unleash a conspiracy theory boom.\n",
-    "3.  *Bias*--When a traditionally African-American name is searched for on Google, it displays ads for criminal background checks.\n",
+    "1.  *Recourse processes*—Arkansas's buggy healthcare algorithms left patients stranded.\n",
+    "2.  *Feedback loops*—YouTube's recommendation system helped unleash a conspiracy theory boom.\n",
+    "3.  *Bias*—When a traditionally African-American name is searched for on Google, it displays ads for criminal background checks.\n",
     "\n",
     "In fact, for every concept that we introduce in this chapter, we are going to provide at least one specific example. For each one, think about what you could have done in this situation, and what kinds of obstructions there might have been to you getting that done. How would you deal with them? What would you look out for?"
    ]
@@ -180,7 +180,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "But this was not an isolated incident--the organization's involvement was extensive. IBM and its subsidiaries provided regular training and maintenance onsite at the concentration camps: printing off cards, configuring machines, and repairing them as they broke frequently. IBM set up categorizations on its punch card system for the way that each person was killed, which group they were assigned to, and the logistical information necessary to track them through the vast Holocaust system. IBM's code for Jews in the concentration camps  was 8: some 6,000,000 were killed. Its code for Romanis was 12 (they were labeled by the Nazis as \"asocials,\" with over 300,000 killed in the *Zigeunerlager*, or “Gypsy camp”). General executions were coded as 4, death in the gas chambers as 6."
+    "But this was not an isolated incident—the organization's involvement was extensive. IBM and its subsidiaries provided regular training and maintenance onsite at the concentration camps: printing off cards, configuring machines, and repairing them as they broke frequently. IBM set up categorizations on its punch card system for the way that each person was killed, which group they were assigned to, and the logistical information necessary to track them through the vast Holocaust system. IBM's code for Jews in the concentration camps  was 8: some 6,000,000 were killed. Its code for Romanis was 12 (they were labeled by the Nazis as \"asocials,\" with over 300,000 killed in the *Zigeunerlager*, or “Gypsy camp”). General executions were coded as 4, death in the gas chambers as 6."
    ]
   },
   {
@@ -200,13 +200,13 @@
     "\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 was revealed to have cheated on its 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",
-    "Of course, it's not all bad--if a project you are involved in turns out to make a huge positive impact on even one person, this is going to make you feel pretty great!\n",
+    "Of course, it's not all bad—if a project you are involved in turns out to make a huge positive impact on even one person, this is going to make you feel pretty great!\n",
     "\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 models better by optimizing some metric or other. But optimizing that metric may not actually lead to better outcomes. And even if it *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 might not 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 to do it--so if you don’t do it, the best person isn’t working on that project. If the first five people they ask all say no too, even better!"
+    "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 to do it—so if you don’t do it, the best person isn’t working on that project. If the first five people they ask all say no too, even better!"
    ]
   },
   {
@@ -816,7 +816,7 @@
    "source": [
     "The hiring process is particularly broken in tech. One study indicative of the disfunction comes from Triplebyte, a company that helps place software engineers in companies, conducting a standardized technical interview as part of this process. They have a fascinating dataset: the results of how over 300 engineers did on their exam, coupled with the results of how those engineers did during the interview process for a variety of companies. The number one finding from [Triplebyte’s research](https://triplebyte.com/blog/who-y-combinator-companies-want) is that “the types of programmers that each company looks for often have little to do with what the company needs or does. Rather, they reflect company culture and the backgrounds of the founders.”\n",
     "\n",
-    "This is a challenge for those trying to break into the world of deep learning, since most companies' deep learning groups today were founded by academics. These groups tend to look for people \"like them\"--that is, people that can solve complex math problems and understand dense jargon. They don't always know how to spot people who are actually good at solving real problems using deep learning.\n",
+    "This is a challenge for those trying to break into the world of deep learning, since most companies' deep learning groups today were founded by academics. These groups tend to look for people \"like them\"—that is, people that can solve complex math problems and understand dense jargon. They don't always know how to spot people who are actually good at solving real problems using deep learning.\n",
     "\n",
     "This leaves a big opportunity for companies that are ready to look beyond status and pedigree, and focus on results!"
    ]
@@ -945,7 +945,7 @@
     "\n",
     "It's reassuring that Angwin thinks we are largely still in the diagnosis phase: if your understanding of these problems feels incomplete, that is normal and natural. Nobody has a “cure” yet, although it is vital that we continue working to better understand and address the problems we are facing.\n",
     "\n",
-    "One of our reviewers for this book, Fred Monroe, used to work in hedge fund trading. He told us, after reading this chapter, that many of the issues discussed here (distribution of data being dramatically different than what a model was trained on, the impact feedback loops on a model once deployed and at scale, and so forth) were also key issues for building profitable trading models. The kinds of things you need to do to consider societal consequences are going to have a lot of overlap with things you need to do to consider organizational, market, and customer consequences--so thinking carefully about ethics can also help you think carefully about how to make your data product successful more generally!"
+    "One of our reviewers for this book, Fred Monroe, used to work in hedge fund trading. He told us, after reading this chapter, that many of the issues discussed here (distribution of data being dramatically different than what a model was trained on, the impact feedback loops on a model once deployed and at scale, and so forth) were also key issues for building profitable trading models. The kinds of things you need to do to consider societal consequences are going to have a lot of overlap with things you need to do to consider organizational, market, and customer consequences—so thinking carefully about ethics can also help you think carefully about how to make your data product successful more generally!"
    ]
   },
   {
@@ -1004,7 +1004,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Section 1: That's a Wrap!"
+    "## Deep Learning in Practice: That's a Wrap!"
    ]
   },
   {