(upbeat music) - [Interviewer] Alright DJ, you recently called for a code of ethics in data science. What does that mean? - Almost all fields have some notion of what does it mean to take responsibility for your actions. Most well known one is in medicine, the Hippocratic oath, to do no harm. There's versions in engineering.
There's been versions in law. Almost every place has one. What does it look like for a technologist? What would it be to have an oath that we took? What does it look like to adhere to that? What happens when we do not adhere to it? Part of it is in the discussion of what an oath is, it's more than what the actual oath is because if forcing us to decide how do we want to be, as a community of people who use data? What does it mean for the rest of society to say, what are you people doing? Do we agree with how you're approaching the world? Do we like that you can do these things with technology? That interface is one that we have to get ahead of now because not only of things that we started to see happening, everywhere from Russian interference in the US elections, to people starting to abuse data in ways to make money, or to create fraud, or all these other things.
We have to start having that dialog now. But it's more than just an oath. An oath just starts to put the top level in there. There has to be this idea of what are good best practices. What are the principles we adhere to? While that may be very obvious for a tight knit group of industries that are within close geography to each other, so there's a lot of cross pollination, how do we make sure that happens worldwide? So that best practices that are happening maybe somewhere in Africa actually do get populated to the United States? I say it very concretely that way because we can't assume that best practices are always going to happen from Silicon Valley.
Many times they're coming from everywhere else in the world and we should be open minded to adopt them fast. How do we iterate as new ideas come about? And these questions of well, oh, we haven't thought about that. How do we track and how do we make sure there's not bias in there? (upbeat music)
Skill Level Appropriate for all
Bracketology Club: Using March Madness to Learn Data Sciencewith Brian Tonsoni12m 7s Appropriate for all
The Data Science of Government and Political Science, with Barton Poulsonwith Barton Poulson1h 2m Appropriate for all
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