Next, I'd like to turn to the high level aims that I have for you as the students in taking this class. As opposed to a lot of the courses you take, where those high-level aims are likely specific technical abilities and skills on the computing or the mathematical side. In a course like this, the aims are more at the high cognitive level, and they're really about developing a perspective and a basis for dealing with ethical issues that may arise during your entire career, whether that says a data scientist or in whatever you do. As this simple slide shows, there's really three parts to that. The highest level one is simply developing awareness, helping the set of antennae that you may have that go off in certain situations and say, "Hey, that thing I'm working on, it really has some ethical issues, and I, and the people I worked with should look at those things." The second one is then developing a tool-set for dealing with those ethical questions and learning that there's more to dealing with ethical questions than just our common sense, that there are frameworks. Frameworks that go back millennia that people have developed for dealing with ethics, and if they don't all agree with each other. That they give different perspectives, but it's good to understand them and decide what's most applicable to a particular situation. That'll be the focus of the next couple of classes. Then the third part, which is the majority of this course, is really starting to apply the first two. To look at a broad set of applications in Data Science where ethical issues come up to understand what those issues are and to practice, if you will, applying the tool-set so that we get more experience in making ethical determinations. Next, I'll summarize the topics that this course will cover. The course is divided into five modules, and the topics of each of those modules are listed on this next slide. Of course, this is a fairly brief course. What I've tried to do is select a set of topics that cover the range of what you should have for basis of understanding ethical issues in data science, and in each case give you some debt. But on a selected set of topics within the topic of that module. The first module is one I just alluded to the ethical theories that are useful as a foundation for assessing real-world situations of Ethics and Data Science. There's three that tend to be used most frequently in this context and others, and we will talk about each of those three briefly. One is called either Kantianism or deontology. Those are two words for the same theory. The second is called virtue ethics, and the third is called utilitarianism. As I alluded to just before, these are really quite different approaches to ethics and we will understand how they can come to different conclusions in some cases similar and others by examining some case studies as we examine these theories. Next is the second module. We're going to turn to the internet, which as I said, is really the foundation of so many of the ethical issues that we face in data science and computing more generally. Looking at the Internet will look at issues of both privacy and security. First of all, look at some online applications and example being these types of systems that most of you probably see every day making recommendations, whether they're recommendations for purchases or songs to listen to or videos to look at or whatever and looking at the implications, sometimes very subtle implications in terms of both privacy and security of those sorts of applications. Next, we will look at internet security, which is a really different topic. It's a technical topic, but it's very important for you as a data scientist to understand a different sort of ethics. That is that there are people who are trying to do bad things to computers, to break into computers that we would hope would be secure. For you to have some understanding of the causes of those breaches and the things as data scientists that you can do to avoid them. I should point out that each of these two topics, like many of the others in the class, could be entire courses by themselves. But we will try to give you some background and some depth in each of them. Third, in what really is the core of this course, we're going to look at Professional Ethics. Ethics in the context of a data science or computing career. First, we will look at professional codes of ethics. Most professional societies have carefully considered codes of ethics and we will consider one of them, the ACM code of professional ethics, and look at that in the context of case studies. Next, we'll look at some contemporary issues that have arisen in tech companies that have significant ethical aspects to them. Some of these are ones that have been much followed in the media and you may have seen them, and we'll look at them in the context of what we've studied. Then finally in what may be the most useful part of this course, I'm going to ask each of you to try to find a Computing Professional or Data Science Professional and talk to them about the ethical issues that they've encountered throughout their career. Then share what you've learned through discussion groups with other people in this course. The fourth topic we'll cover is the one that people tend to think of the most when they think of ethical issues in data science and that's algorithmic bias. The facial recognition example that I gave a couple of minutes ago is an example of batch. In general, algorithmic bias means the biases that may occur in any decision-making system that computers use. Usually decisions that are based on a form of artificial intelligence and machine learning. These are usually the systems that involve very large data-sets and making conclusions and drawing inferences based on those very large data-sets. We'll provide a little background on how these systems work. Something that you'll learn much more of when you take AI or Machine Learning class and then we'll delve into too important applications. One, biases in a variety of applications related to gender and race and then the second one, facial recognition. Finally, I've decided to focus on just one of the many important application areas that data science is involved in, medical and healthcare applications. This will allow us to do a few really interesting things. One is to focus on the pros and cons of some of the AI aided approaches to health care, which is a core instance of applying ethical issues in data science. The second is much more futurist type of topic looking at some of the things that are being talked about for the future, such as gene editing and neurological interventions, which are at their heart, ethical issues. Their heart have ethical issues. Then finally, we're going to touch on a topic that I think anybody who studies ethical issues in any computing related field should think about a little bit more, which is the future of work and we'll use health care and Data Science in health care as a motivation for looking at a bit of the future of work. Now let me turn to how this course will work and what you as students will need to do for it. Ideally, ethics is a topic that is best approached through intensive discussion. Discussion related to case studies and other issues. You can think of yourself as one of the ancient Greeks seated under a tree and talking about philosophical issues. I will encourage you to do that, to engage in the discussion group that goes along with this class online and to share your perspectives and to learn from the perspectives of your fellow students. In conjunction, there'll be a reasonable bit of reading for this course, but it's basically short and simple things to read. These will be readings mainly from popular media, whether that's newspapers, magazines, blogs about a particular topic that's come up in the world of computing and data science that involves ethical issues. I strongly encourage you to read those. There'll be listed along with the lessons for the course. For the next two classes, the readings will be a little bit more conventional academic readings about the ethical theories that we'll be studying. Then finally, this is not a subject that is all that well-suited to quizzes or tests. There'll be a little bit of that for the students who are taking this course for credit but in that case, the main work will be participating in discussion groups and writing a few reports, both based on case studies that you look at and also based on the interview that I alluded to of a data science Professional. An important note for students who are taking, or considering taking this course for credit please be sure to respond to all the discussion prompts and savior responses as you will need them for the final exam. Before the next class, there are three things that I would encourage you to do. The first is to get into the class discussion group and just introduce yourself a little, tell us things about you that you would like to share and like us to know. The second if you choose, is to do a little bit of detective work. Look at one student who' has introduced themselves other than yourself, and see what you can find about that person online. That really gets to a part of the heart of ethics issues in computing and Data Science. What we're making available for ourselves and you might ask yourself, am finding something out about that person that they wouldn't want? For instance, a job interviewer or to know about them. If you do, you might share it with that person, but don't do it publicly. Then the final thing is, depends on your learning style really. In the next class will be talking about the first two of the ethical theories, Kantianism or deontology and virtue ethics and there'll be reading about each of those and depending on what you prefer to do, you might want do that reading before viewing the lecture or after viewing the video. Thanks for joining me with this first class and I look forward to really diving in the next one.
