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Merge pull request #30 from bradsaracik/proofread-BradS03
proofread ethics
This commit is contained in:
Jeremy Howard
GitHub
commit
1d091931b5
@ -25,14 +25,14 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This chapter was co-authored by Dr Rachel Thomas, the co-founder of fast.ai, and founding director of the Center for Applied Data Ethics at the University of San Francisco. It largely follows a subset of her syllabus for the \"Introduction to Data Ethics\" course that she developed."
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"This chapter was co-authored by Dr Rachel Thomas, the co-founder of fast.ai, and founding director of the Center for Applied Data Ethics at the University of San Francisco. It largely follows a subset of the syllabus she developed for the \"Introduction to Data Ethics\" course."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"As we discussed in chapters 1 and 2, sometimes, machine learning models can go wrong. They can have bugs. They can be presented with data that they haven't seen before, and behave in ways we don't expect. Or, they could work exactly as designed, but be used for something that you would much prefer they were never ever used for.\n",
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"As we discussed in Chapters 1 and 2, sometimes, machine learning models can go wrong. They can have bugs. They can be presented with data that they haven't seen before, and behave in ways we don't expect. Or, they could work exactly as designed, but be used for something that you would much prefer they were never ever used for.\n",
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"\n",
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"Because deep learning is such a powerful tool and can be used for so many things, it becomes particularly important that we consider the consequences of our choices. The philosophical study of *ethics* is the study of right and wrong, including how we can define those terms, recognise right and wrong actions, and understand the connection between actions and consequences. The field of *data ethics* has been around for a long time, and there are many academics focused on this field. It is being used to help define policy in many jurisdictions; it is being used in companies big and small to consider how best to ensure good societal outcomes from product development; and it is being used by researchers who want to make sure that the work they are doing is used for good, and not for bad.\n",
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"\n",
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@ -43,7 +43,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"> j: At university, philosophy of ethics was my main thing (it would have been the topic of my thesis, if I'd finished it, instead of dropping out to join the real-world). Based on the years I spent studying ethics, I can tell you this: no one really agrees on what right and wrong are, whether they exist, how to spot them, which people are good, and which bad, or pretty much anything else. So don't expect too much from the theory! We're going to focus on examples and thoughts starters here, not theory."
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"> j: At university, philosophy of ethics was my main thing (it would have been the topic of my thesis, if I'd finished it, instead of dropping out to join the real-world). Based on the years I spent studying ethics, I can tell you this: no one really agrees on what right and wrong are, whether they exist, how to spot them, which people are good, and which bad, or pretty much anything else. So don't expect too much from the theory! We're going to focus on examples and thought starters here, not theory."
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]
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},
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{
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@ -93,7 +93,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The Verge investigated software used in over half of U.S. states to determine how much healthcare people receive, and documented their findings in an article [What Happens When an Algorithm Cuts Your Healthcare](https://www.theverge.com/2018/3/21/17144260/healthcare-medicaid-algorithm-arkansas-cerebral-palsy). After implementation of the algorithm in Arkansas, people (many with severe disabilities) drastically had their healthcare cut. For instance, Tammy Dobbs, a woman with cerebral palsy who needs an aid to help her to get out of bed, to go to the bathroom, to get food, and more, had her hours of help suddenly reduced by 20 hours a week. She couldn’t get any explanation for why her healthcare was cut. Eventually, a court case revealed that there were mistakes in the software implementation of the algorithm, negatively impacting people with diabetes or cerebral palsy. However, Dobbs and many other people reliant on these health care benefits live in fear that their benefits could again be cut suddenly and inexplicably."
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"The Verge investigated software used in over half of the U.S. states to determine how much healthcare people receive, and documented their findings in an article [What Happens When an Algorithm Cuts Your Healthcare](https://www.theverge.com/2018/3/21/17144260/healthcare-medicaid-algorithm-arkansas-cerebral-palsy). After implementation of the algorithm in Arkansas, people (many with severe disabilities) drastically had their healthcare cut. For instance, Tammy Dobbs, a woman with cerebral palsy who needs an aid to help her to get out of bed, to go to the bathroom, to get food, and more, had her hours of help suddenly reduced by 20 hours a week. She couldn’t get any explanation for why her healthcare was cut. Eventually, a court case revealed that there were mistakes in the software implementation of the algorithm, negatively impacting people with diabetes or cerebral palsy. However, Dobbs and many other people reliant on these health care benefits live in fear that their benefits could again be cut suddenly and inexplicably."
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]
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},
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{
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@ -139,7 +139,7 @@
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"source": [
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"Being a computer scientist, she studied this systematically, and looked at over 2000 names. She found that this pattern held where historically black names received advertisements suggesting that the person had a criminal record. Whereas, white names had more neutral advertisements.\n",
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"\n",
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"This is an example of bias. It can make a big difference to people's lives — for instance, if a job applicant is googled that it may appear that they have a criminal record when they do not."
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"This is an example of bias. It can make a big difference to people's lives — for instance, if a job applicant is googled then it may appear that they have a criminal record when they do not."
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]
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},
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{
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@ -153,7 +153,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"One very natural reaction to considering these issues is: \"So what? What's that got to do with me? I'm a data scientist, not a politician. I'm not the senior executive at my company who make the decisions about what we do. I'm just trying to build the most predictive model I can.\"\n",
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"One very natural reaction to considering these issues is: \"So what? What's that got to do with me? I'm a data scientist, not a politician. I'm not one of the senior executives at my company who make the decisions about what we do. I'm just trying to build the most predictive model I can.\"\n",
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"\n",
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"These are very reasonable questions. But we're going to try to convince you that the answer is: everybody who is training models absolutely needs to consider how their model will be used. And to consider how to best ensure that it is used as positively as possible. There are things you can do. And if you don't do these things, then things can go pretty bad.\n",
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"\n",
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@ -187,15 +187,15 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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",
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"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 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",
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"\n",
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"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",
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"\n",
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"It's not just a moral burden either. Sometimes, technologists very directly pay 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",
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"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",
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"\n",
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"On the other hand, 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",
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"\n",
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"Okay, so hopefully we have convinced you that you ought to care. Now the question is: can you actually do anything can you make an impact beyond just maximising the predictive power of your models? Consider the pipeline are 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. 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. It's especially tricky if you're involved in data collection, which is even earlier in the pipeline.\n",
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"Okay, so hopefully we have convinced you that you ought to care. Now the question is: can you actually do anything that makes an impact beyond just maximising the predictive power of your models? Consider the pipeline of steps that occur 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. 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. It's especially tricky if you're involved in data collection, which is even earlier in the pipeline.\n",
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"\n",
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"Data often ends up being used for different purposes than why it was originally collected. 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.”"
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]
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@ -215,7 +215,7 @@
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"\n",
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"These are not just algorithm questions. They are data product design questions. But the product managers, executives, judges, journalists, doctors… whoever ends up developing and using the system of which your model is a part will not be well-placed to understand the decisions that you made, let alone change them.\n",
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"\n",
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"For instance, two studies found that Amazon’s facial recognition software produced [inaccurate](https://www.nytimes.com/2018/07/26/technology/amazon-aclu-facial-recognition-congress.html) and [racially biased results](https://www.theverge.com/2019/1/25/18197137/amazon-rekognition-facial-recognition-bias-race-gender). Amazon claimed that the researchers should have changed the default parameters, they did not explain how it would change the racially baised results. Further more, it turned out that [Amazon was not instructing police departments](https://gizmodo.com/defense-of-amazons-face-recognition-tool-undermined-by-1832238149) that use its software to do this either. There was, presumably, a big distance between the researchers that developed these algorithms, and the Amazon documentation staff that wrote the guidelines provided to the police. A lack of tight integration led to serious problems for society, the police, and Amazon themselves. It turned out that their system erroneously *matched* 28 members of congress to criminal mugshots! (And these members of congress wrongly matched to criminal mugshots disproportionately included people of color as seen in <<congressmen>>.)"
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"For instance, two studies found that Amazon’s facial recognition software produced [inaccurate](https://www.nytimes.com/2018/07/26/technology/amazon-aclu-facial-recognition-congress.html) and [racially biased results](https://www.theverge.com/2019/1/25/18197137/amazon-rekognition-facial-recognition-bias-race-gender). Amazon claimed that the researchers should have changed the default parameters, they did not explain how it would change the racially baised results. Furthermore, it turned out that [Amazon was not instructing police departments](https://gizmodo.com/defense-of-amazons-face-recognition-tool-undermined-by-1832238149) that used its software to do this either. There was, presumably, a big distance between the researchers that developed these algorithms, and the Amazon documentation staff that wrote the guidelines provided to the police. A lack of tight integration led to serious problems for society, the police, and Amazon themselves. It turned out that their system erroneously *matched* 28 members of congress to criminal mugshots! (And these members of congress wrongly matched to criminal mugshots disproportionately included people of color as seen in <<congressmen>>.)"
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]
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},
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{
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@ -231,9 +231,9 @@
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"source": [
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"Data scientists need to be part of a cross disciplinary team. And researchers need to work closely with the kinds of people who will end up using their research. Better still is if the domain experts themselves have learnt enough to be able to train and debug some models themselves — hopefully there's a few of you reading this book right now!\n",
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"\n",
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"The modern workplace is a very specialised place. Everybody tends to have very well-defined jobs to perform. Especially in large companies, it can be very hard to know what all the pieces of the puzzle are. Sometimes companies even intentionally obscure the overall project goals that are being worked on, if they know that their employees are not going to like the answers. This is sometimes done by compartmentalising every piece as much as possible\n",
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"The modern workplace is a very specialised place. Everybody tends to have very well-defined jobs to perform. Especially in large companies, it can be very hard to know what all the pieces of the puzzle are. Sometimes companies even intentionally obscure the overall project goals that are being worked on, if they know that their employees are not going to like the answers. This is sometimes done by compartmentalising pieces as much as possible\n",
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"\n",
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"In other words, we're not saying that any of this is easy. It's hard. It's really hard. We all have to do our best. And with often seen that the people who do get involved in the higher-level context of these projects, and attempt to develop cross disciplinary capabilities and teams, become some of the most important and well rewarded parts of their organisations. It's the kind of work that tends to be highly appreciated by senior executives, even if it is considered, sometimes, rather uncomfortable by middle management."
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"In other words, we're not saying that any of this is easy. It's hard. It's really hard. We all have to do our best. And we have often seen that the people who do get involved in the higher-level context of these projects, and attempt to develop cross disciplinary capabilities and teams, become some of the most important and well rewarded parts of their organisations. It's the kind of work that tends to be highly appreciated by senior executives, even if it is considered, sometimes, rather uncomfortable by middle management."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In a complex system it is easy for no one person to feel responsible for outcomes. While this is understandable, it does not lead to good results. In the earlier example of the Arkansas healthcare system in which a bug led to people with cerebral palsy losing access to needed care, the creator of the algorithm blamed government officials, and government officials could blame those who implemented the software. NYU professor danah boyd described this phenomenon: \"bureaucracy has often been used to evade responsibility, and today's algorithmic systems are extending bureaucracy.\"\n",
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"In a complex system, it is easy for no one person to feel responsible for outcomes. While this is understandable, it does not lead to good results. In the earlier example of the Arkansas healthcare system in which a bug led to people with cerebral palsy losing access to needed care, the creator of the algorithm blamed government officials, and government officials could blame those who implemented the software. NYU professor Danah Boyd described this phenomenon: \"bureaucracy has often been used to evade responsibility, and today's algorithmic systems are extending bureaucracy.\"\n",
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"\n",
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"An additional reason why recourse is so necessary, is because data often contains errors. Mechanisms for audits and error-correction are crucial. A database of suspected gang members maintained by California law enforcement officials was found to be full of errors, including 42 babies who had been added to the database when they were less than 1 year old (28 of whom were marked as “admitting to being gang members”). In this case, there was no process in place for correcting mistakes or removing people once they’ve been added. Another example is the US credit report system; in a large-scale study of credit reports by the FTC in 2012, it was found that 26% of consumers had at least one mistake in their files, and 5% had errors that could be devastating. Yet, the process of getting such errors corrected is incredibly slow and opaque. When public-radio reporter Bobby Allyn discovered that he was erroneously listed as having a firearms conviction, it took him \"more than a dozen phone calls, the handiwork of a county court clerk and six weeks to solve the problem. And that was only after I contacted the company’s communications department as a journalist.\" (as covered in the article [How the careless errors of credit reporting agencies are ruining people’s lives](https://www.washingtonpost.com/posteverything/wp/2016/09/08/how-the-careless-errors-of-credit-reporting-agencies-are-ruining-peoples-lives/))\n",
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"An additional reason why recourse is so necessary, is because data often contains errors. Mechanisms for audits and error-correction are crucial. A database of suspected gang members maintained by California law enforcement officials was found to be full of errors, including 42 babies who had been added to the database when they were less than 1 year old (28 of whom were marked as “admitting to being gang members”). In this case, there was no process in place for correcting mistakes or removing people once they’ve been added. Another example is the US credit report system; in a large-scale study of credit reports by the FTC (Federal Trade Commission) in 2012, it was found that 26% of consumers had at least one mistake in their files, and 5% had errors that could be devastating. Yet, the process of getting such errors corrected is incredibly slow and opaque. When public-radio reporter Bobby Allyn discovered that he was erroneously listed as having a firearms conviction, it took him \"more than a dozen phone calls, the handiwork of a county court clerk and six weeks to solve the problem. And that was only after I contacted the company’s communications department as a journalist.\" (as covered in the article [How the careless errors of credit reporting agencies are ruining people’s lives](https://www.washingtonpost.com/posteverything/wp/2016/09/08/how-the-careless-errors-of-credit-reporting-agencies-are-ruining-peoples-lives/))\n",
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"\n",
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"As machine learning practitioners, we do not always think of it as our responsibility to understand how our algorithms and up being implemented in practice. But we need to."
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]
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We have already explained in <<chapter_intro>> how an algorithm can interact with its enviromnent to create a feedback loop, making prediction that reinforces actions taken in the field, which lead to predictions even more pronounced in the same direciton. The New York Times published another article on YouTube's recommendation system, titled [On YouTube’s Digital Playground, an Open Gate for Pedophiles](https://www.nytimes.com/2019/06/03/world/americas/youtube-pedophiles.html). The article started with this chilling story:"
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"We have already explained in <<chapter_intro>> how an algorithm can interact with its enviromnent to create a feedback loop, making predictions that reinforce actions taken in the real world, which lead to predictions even more pronounced in the same direciton. The New York Times published another article on YouTube's recommendation system, titled [On YouTube’s Digital Playground, an Open Gate for Pedophiles](https://www.nytimes.com/2019/06/03/world/americas/youtube-pedophiles.html). The article started with this chilling story:"
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]
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Discussions of bias online tend to get pretty confusing pretty fast. The word bias mean so many different things. Statisticians often think that when data ethicists are talking about bias that they're talking about the statistical definition of the term bias. But they're not. And they're certainly not talking about the bias is that appear in the weights and bias is which are the parameters of your model!\n",
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"Discussions of bias online tend to get pretty confusing pretty fast. The word bias means so many different things. Statisticians often think that when data ethicists are talking about bias that they're talking about the statistical definition of the term bias. But they're not. And they're certainly not talking about the biases that appear in the weights and biases which are the parameters of your model!\n",
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"\n",
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"What they're talking about is the social science concept of bias. In [A Framework for Understanding Unintended Consequences of Machine Learning](https://arxiv.org/abs/1901.10002) MIT's Suresh and Guttag describe six types of bias in machine learning, summarized in <<bias>> from their paper."
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We'll discuss the four of these types of bias here that we've found most helpful in our own work (see the paper for details on the others)."
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"We'll discuss four of these types of bias, those that we've found most helpful in our own work (see the paper for details on the others)."
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]
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},
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{
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" - When doctors were shown identical files, they were much less likely to recommend cardiac catheterization (a helpful procedure) to Black patients\n",
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" - When bargaining for a used car, Black people were offered initial prices $700 higher and received far smaller concessions\n",
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" - Responding to apartment-rental ads on Craigslist with a Black name elicited fewer responses than with a white name\n",
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" - An all-white jury was 16 points more likely to convict a Black defendant than a white one, but when a jury had 1 Black member, it convicted both at same rate.\n",
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" - An all-white jury was 16 percentage points more likely to convict a Black defendant than a white one, but when a jury had 1 Black member, it convicted both at same rate.\n",
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"\n",
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"The COMPAS algorithm, widely used for sentencing and bail decisions in the US, is an example of an important algorithm which, when tested by ProPublica, showed clear racial bias in practice:"
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]
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Any dataset involving humans can have this kind of bias, such as medical data, sales data housing data, political data, and so on. Because underlying bias is so pervasive, bias in datasets is very pervasive. Racial bias even turns up in computer vision, as shown in this example of auto-categorized photos shared on Twitter by a Google Photos user:"
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"Any dataset involving humans can have this kind of bias, such as medical data, sales data, housing data, political data, and so on. Because underlying bias is so pervasive, bias in datasets is very pervasive. Racial bias even turns up in computer vision, as shown in this example of auto-categorized photos shared on Twitter by a Google Photos user:"
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<img src=\"images/ethics/image10.png\" id=\"image_provenance\" caption=\"Image provenance in popular training sets\" alt=\"Grpahs showing how the vast majority of images in porpular training dataset come from the US or Western Europe\" width=\"800\">"
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"<img src=\"images/ethics/image10.png\" id=\"image_provenance\" caption=\"Image provenance in popular training sets\" alt=\"Graphs showing how the vast majority of images in popular training datasets come from the US or Western Europe\" width=\"800\">"
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<img src=\"images/ethics/image11.png\" width=\"600\">"
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"<img src=\"images/ethics/image11.png\" id=\"turkish_gender\" caption=\"Gender bias in text data sets\" alt=\"Figure showing gender bias in data sets used to train language models showing up in translations\" width=\"600\">"
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]
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},
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{
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"source": [
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"We also see this kind of bias in online advertisements. For instance, a study in 2019 found that even when the person placing the ad does not intentionally discriminate, Facebook will show the ad to very different audiences used on race and gender. Housing ads with the same text, but changing the picture, between a white family and a black family, were shown to racially different audiences."
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"We also see this kind of bias in online advertisements. For instance, a study in 2019 found that even when the person placing the ad does not intentionally discriminate, Facebook will show the ad to very different audiences based on race and gender. Housing ads with the same text, but changing the picture between a white or black family, were shown to racially different audiences."
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In the paper [Does Machine Learning Automate Moral Hazard and Error](https://scholar.harvard.edu/files/sendhil/files/aer.p20171084.pdf) in *American Economic Review*, the authors look at a model that tries to answer the question: using historical EHR data, what factors are most predictive of stroke? This are the top predictors from the model:\n",
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"In the paper [Does Machine Learning Automate Moral Hazard and Error](https://scholar.harvard.edu/files/sendhil/files/aer.p20171084.pdf) in *American Economic Review*, the authors look at a model that tries to answer the question: using historical EHR data, what factors are most predictive of stroke? These are the top predictors from the model:\n",
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"\n",
|
||||
" - Prior Stroke\n",
|
||||
" - Cardiovascular disease\n",
|
||||
@ -579,7 +579,7 @@
|
||||
"source": [
|
||||
"For example, in the training dataset, 14.6% of surgeons were women, yet in the model predictions, only 11.6% of the true positives were women. The model is thus amplifying the bias existing in the training set.\n",
|
||||
"\n",
|
||||
"Now that we saw those bias existed, what can we do to mitigate them?"
|
||||
"Now that we saw those biases existed, what can we do to mitigate them?"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -637,7 +637,7 @@
|
||||
"source": [
|
||||
"*Disinformation* has a history stretching back hundreds or even thousands of years. It is not necessarily about getting someone to believe something false, but rather, often to sow disharmony and uncertainty, and to get people to give up on seeking the truth. Receiving conflicting accounts can lead people to assume that they can never know what to trust.\n",
|
||||
"\n",
|
||||
"Some people think disinformation is primarily about false information or *fake news*, but in reality, disinformation can often contain seeds of truth, or involve half-truths taken out of context. Ladislav Bittman, who was an intelligence officer in the USSR who later defected to the United States and wrote some books in the 1970s and 1980s on the role of disinformation in Soviet propaganda operations. He said, \"Most campaigns are a carefully designed mixture of facts, half-truths, exaggerations, & deliberate lies.\"\n",
|
||||
"Some people think disinformation is primarily about false information or *fake news*, but in reality, disinformation can often contain seeds of truth, or involve half-truths taken out of context. Ladislav Bittman was an intelligence officer in the USSR who later defected to the United States and wrote some books in the 1970s and 1980s on the role of disinformation in Soviet propaganda operations. He said, \"Most campaigns are a carefully designed mixture of facts, half-truths, exaggerations, & deliberate lies.\"\n",
|
||||
"\n",
|
||||
"In the United States this has hit close to home in recent years, with the FBI detailing a massive disinformation campaign linked to Russia in the 2016 US election. Understanding the disinformation that was used in this campaign is very educational. For instance, the FBI found that the Russian disinformation campaign often organized two separate fake *grass roots* protests, one for each side of an issue, and got them to protest at the same time! The Houston Chronicle reported on one of these odd events:\n",
|
||||
"\n",
|
||||
@ -718,7 +718,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"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 processes to implement."
|
||||
"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."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -791,7 +791,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Currently, less than 12% of AI researchers are women, according to a study from element AI. The statistics are similarly dire when it comes to race and age. When everybody on a team has similar backgrounds, there are likely to have similar blindspots around ethical risks. The Harvard Business Review (HBR) has published a number of studies showing many benefits of diverse teams, including:\n",
|
||||
"Currently, less than 12% of AI researchers are women, according to a study from element AI. The statistics are similarly dire when it comes to race and age. When everybody on a team has similar backgrounds, they are likely to have similar blindspots around ethical risks. The Harvard Business Review (HBR) has published a number of studies showing many benefits of diverse teams, including:\n",
|
||||
"\n",
|
||||
"- [How Diversity Can Drive Innovation](https://hbr.org/2013/12/how-diversity-can-drive-innovation)\n",
|
||||
"- [Teams Solve Problems Faster When They’re More Cognitively Diverse](https://hbr.org/2017/03/teams-solve-problems-faster-when-theyre-more-cognitively-diverse)\n",
|
||||
@ -896,7 +896,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Coming from a background of working with binary logic, the lack of clear answers in ethics can be frustrating at first. Yet, the implications of how our work impacts the work, including unintended consequences and weaponization by bad actors, are some of the most important questions we can (and should!) consider. Even though there aren't any easy answers, there are definite pitfalls to avoid and practices to move towards more ethical behavior.\n",
|
||||
"Coming from a background of working with binary logic, the lack of clear answers in ethics can be frustrating at first. Yet, the implications of how our work impacts the world, including unintended consequences and the work becoming weaponization by bad actors, are some of the most important questions we can (and should!) consider. Even though there aren't any easy answers, there are definite pitfalls to avoid and practices to move towards more ethical behavior.\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 was trained on, impact of model and feedback loops 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 too--so thinking carefully about ethics can also help you think carefully about how to make your data product successful more generally!"
|
||||
]
|
||||
@ -966,7 +966,7 @@
|
||||
"source": [
|
||||
"Congratulations! You've made it to the end of the first section of the book. In this section we've tried to show you what deep learning can do, and how you can use it to create real applications and products. At this point, you will get a lot more out of the book if you spend some time trying out what you've learnt. Perhaps you have already been doing this as you go along — in which case, great! But if not, that's no problem either… Now is a great time to start experimenting yourself.\n",
|
||||
"\n",
|
||||
"If you haven't been to the book website yet, head over there now. Remember, you can find it here: [book.fast.ai](https;//book.fast.ai). It's really important that you have got yourself set up to run the notebooks. Becoming an effective deep learning practitioner is all about practice. So you need to be training models. So please go get the notebooks running now if you haven't already! And also have a look on the website for any important updates or notices; deep learning changes fast, and we can't change the words that in this book, so the website is where you need to look to ensure you have the most up-to-date information.\n",
|
||||
"If you haven't been to the book website yet, head over there now. Remember, you can find it here: [book.fast.ai](https://book.fast.ai). It's really important that you have got yourself set up to run the notebooks. Becoming an effective deep learning practitioner is all about practice. So you need to be training models. So please go get the notebooks running now if you haven't already! And also have a look on the website for any important updates or notices; deep learning changes fast, and we can't change the words that are printed in this book, so the website is where you need to look to ensure you have the most up-to-date information.\n",
|
||||
"\n",
|
||||
"Make sure that you have completed the following steps:\n",
|
||||
"\n",
|
||||
@ -995,33 +995,8 @@
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.5"
|
||||
},
|
||||
"toc": {
|
||||
"base_numbering": 1,
|
||||
"nav_menu": {},
|
||||
"number_sections": false,
|
||||
"sideBar": true,
|
||||
"skip_h1_title": true,
|
||||
"title_cell": "Table of Contents",
|
||||
"title_sidebar": "Contents",
|
||||
"toc_cell": false,
|
||||
"toc_position": {},
|
||||
"toc_section_display": true,
|
||||
"toc_window_display": false
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user