[MUSIC] So one discussion that you see in ethics in general is whether your talking more about principles or more about rules. So principles are these overarching guidelines, but stop short of really trying to specify specific behavior. Other folks believe that at the goal that the right thing to do is to actually write down a firm set of rules that should not be violated and adheres to these rules, constitutes ethical behavior. So I think that I come down fairly strong on the side of principles, because once you get into rules, you're really, I push, let's see, just from the previous slide, if you're want to talk about rules then I want to talk about formal guarantees, mathematical proofs about things. And we'll talk about some of the limits of what you can do with that. And if you're gonna give up on those, it's not clear to me that legislative rules have any extra power. So declaring and agreeing on the principles we should agree to and writing them down perhaps in the form of these codes of conduct I think is the right way to go. What you're doing is appealing to people's beliefs about these topics as opposed to trying to sort of control their behavior. Okay, so I think and in the Barrow example I think demonstrates this where really the ethical principles that were violated. They essentially followed the rules that were applied at a time. And really many of the rules that have been developed since then in terms of how to handle private data we're not necessarily violating. But at several steps along the way there were ethical principles violated, putting their own needs and their own interests ahead of those of the research subjects, the client, and society. And recommendations against doing that do appear in these codes of conduct that we'll look at briefly. So I want to just consider three and what's. It's sort of interesting is that this is not really cherry picked. This is kinda an attempt at finding all the lists, all the codes of conduct that one can find on put out there by organizations representing the data science community. So I won't claim that actually is exhaustive. I'm sure they're popping up all the time and I'm sure there's many that I've missed. But there's not a lot of them. Nor is there one single that everyone's agreed and that's the reason why there's not a lot of them, it's still quite early. The other thing I'll point out is that this first one on this list, the American Statistical Association is just that, the American Statistical Association. It's not a data science organization and so this code of ethics has been around longer, and I think that you can tell just from reading, the maturity is higher. So one question you can ask is whether we need a separate data science code of conduct, different from that of statisticians. Whether you think you should or not, it's useful, I think, to be aware of at least these three and have read through them and sort of consider what points they're bringing to the fore front and what emphasis they're placing on things, and I think it was instructive for me to compare some of these points and I think it will be instructive for you to have seen them at least in this form in the lecture, okay. Okay, so the first one we're going to look at is the code of conduct from the American statistical Association. It has the following top level sections. One of them professionalism, and then the rest of this is broken down by responsibilities to various parties. And the first one is funders, clients, and employers, publications and testimony, research subjects, research team colleagues, other statisticians or statistical practitioners, the community at large in your field, and regarding allegations of misconduct. And then of employers themselves surrounded in your responsibility to the employers, responsibility of an employer of said practitioners. And I really like this breakdown because we'll see a couple other ways just from the divided space and I think it gets a lot muddier. I think thinking specifically about the parties that have the stake holders involved. When you are taking on these projects and thinking about their needs and considering them all at every step along the way, is the right way to think about this. Okay so I just want to show some, in some cases I paraphrased these, in some cases I provided in verbatim and we're not going to initially read every bullet on every page because you can go to the web and do that. But one is I want to make sure that you have access them as you're sort of watching through this video, you have an excuse to kind of look at them and read through them if you wish. And then the other thing I think I'll do is highlight a few of the ones that strike me as relevant or arguably, perhaps violated, in the barrow example we considered. So the first section of the Code of Conduct, from the ASA, is just professionalism corresponds pretty well to the codes of conduct for professional engineering societies as well, where it's sort of how to do the job properly. So, relevance and statistical analyses, remaining current in the practice, bringing a certain level of subject matter expertise using only those methodologies that are appropriate for the data. The one I've highlighted in blue here is guard against the possibility that a predisposition by investigators or data providers might predetermine the analytic result. I think that's unique to statistical and data science, as opposed to engineering. And I think it was relevant perhaps in the Barrow example where it seemed like the sampling procedure and it seemed like the way the results were presented and it seemed like some of the methodologies were designed with a specific outcome in mind. Namely, that the ethnic and cultural identity of the research subjects would be linked to the certain behaviors of the use of alcohol. And so that's a pretty dangerous idea to come in to the study with. Now I'm not making a lean claim that we have proof of that one way or another, but I think that a strong argument could be developed in in favor of that. Okay, so another thing' here is respect the contribution of intellectual property and disclosed conflicts of interest and so on. The last one here is sort of interesting too, which is expert testimony should be given with the same level of vigorous peer view research. So this is you know, when you're adding something to the scientific literature, you jump through all sorts of hoops but if you're talking to your employer just for giving them results and say a power point presentation. Maybe it could be a little looser with the rigor, and the pointers know you shouldn't do that and these things have a lot of ethical topics. Things are sort of obvious but I think crafting the language around, holding it up as a document that people can sort of read internalize so that they start using that language too. It's important even when the basic idea is somewhat obvious. [MUSIC]
