[MUSIC] In these last few we'll just put up here, they are, I'd argue, less relevant for the barrel example, which I haven't highlighted anything but responsibilities to research team colleagues. Inform colleagues from other disciplines about relevant aspects of statistical ethics, making sure that people aren't sort of using your methods, and you haven't conveyed some of these ethical considerations as well. You know them, but you don't convey them, that lack of communication itself is a lack of ethics. Generally promote the proper use of statistics, you have an obligation to convey the proper use of things, when given the opportunity. Reciprocally respect the epical obligations of people from outside your field. If they have a professional code of conduct that they need to follow as an industrial engineer, or as as in other engineering fields, you need to understand that and the professional reporting, and the last one here is compromising statistical validity for expediency. You don't wanna trade off quality for efficiency. Understanding that, of course, approximations are appropriate. So overall what I like about these is that, many of them are not the bland, obvious, generic ethical considerations that you at the highest level. Many of them really do have specific teeth related to statistical analysis. Responsibilities to other statisticians, or statistics practitioners. This is when you're reviewing other people's papers, you're reviewing other people's work, or you're doing performance reviews for other people in the company that perform these skills to make sure you're being sort of fair and appropriate. Assess the methods, not the individuals. Be willing to sort of offer them training. As I mentioned, I think this code of conduct is pretty good because it's pretty complete. It's mature, it's been thought about, and I think this is an interesting stakeholder community that may not be the first thing you think of when you think about a code of ethics, but it is important. Nonetheless, especially if this term data science will persist and become a strong community, making sure you're figuring out opportunities to help each other out, is gonna be increasingly important, okay. And responsibilities of employers, including organizations. The first one I've highlighted here is recognize that the results of valid statistical studies cannot be guaranteed to conform to the expectations of those commissioning this study, and this is again, pretty obvious, but it's also pretty crucial. This is the source of the trouble over the years with perceived or real conflicts of interest where you imagine the look of company should be perfectly justified in funding a study. But what you don't see is the publication bias in effect. If they don't publicize results that didn't come out the way they want, that's a nearly undetectable, but very strong way of steering research to produce the outcome that they want, right? They could fund 20 studies and only publish the one that came out in favor of the results that helped their business. Okay, so the fact that there's a section in here specifically for employers and statisticians where or not they're reading it. I approve of the fact that they include that in there on equal footing with some of the other groups of stakeholders, and some of the others, you can read them here, and I won't just read them out. The second code of conduct that I just quickly want to go through is the Data Science Association, and this one is less mature than the ASA code of conduct, as I've mentioned. So I'm just going to show you the top level categories and let you click the link to read it yourself, and then highlight a couple of differences of what it chooses to emphasize. So here are the categories, the first one is really just terms and definitions and you can take a look at them. The second is the notion of competence, which we saw emphasized pretty strongly in the ASA's code of conduct. Number 3 is interesting because it calls out the tension between legal requirements and what the client asks and also the ethical considerations. And so we've seen this come up more, and more often, where you may be in a position where you need to make a decision about whether you're gonna collect the data or not. Not because you think it might be released or misused by you, or you're not planning on sorta publishing the result. It's only for some internal usage, but it could be subpoenaed, and so you need to consider that if you put your hands on that data at all, you may be required in the future to give it up. In which case you're either faced with violating the law, violating your client's interests, or violating some ethical consideration, okay? Communication with clients they call out as explicit in a category, which I thought was interesting. This is, you know, don't take the problem specification, run away into a hole, do the work, and then come back out and dump the results. You should be in constant communication as you work through the decisions. Again, this is. General enough where it seems obvious, but I think it's interesting to analyze, to consider what they've chosen to emphasize and what they have not chosen to emphasize. Confidential information, we've seen, we've had that covered, conflicts of interest. Number 7 is more on conflicts of interest and confidentiality. Number 8 is more of a generic thing on scientific integrity. I think overall the point I want to make is that trying to break it down by topic seems more difficult and results in kind of a muddier collection of rules than breaking it down by the parties and how they might be harmed, which is what the SA did. Fine. And then the last one I wanna mention is the Certified Analytics Professional. I won't claim that either one of these last two again are necessarily indicative of The organizations that present them aren't necessarily representing the entirety of the science community, but they did take the time to sort of publish these codes of conduct, and therefore, it's worthwhile to be aware of them. Really, the only thing I wanna point out here is that they all, along with ASA, emphasized the parties involved and how they might be harmed. I think the ASA code of conduct did a better job of being exhaustive about that and being clear about that but it's one important take away that if you're going to think about codes of conduct. Think about who has interest in your work and how they might be harmed by your work one way or another. What their interests are and how you can try to meet them. [MUSIC]
