Of Checklists, Ethics, and Data with Emily Miller and Peter Bull - Episode 184


(Tobias Macey) #1

Originally published at: https://www.podcastinit.com/deon-with-emily-miller-and-peter-bull-episode-184/

MP3 Audio [31 MB]Ogg Vorbis Audio [35 MB]DownloadShow URL Summary As data science becomes more widespread and has a bigger impact on the lives of people, it is important that those projects and products are built with a conscious consideration of ethics. Keeping ethical principles in mind throughout the lifecycle of a data project helps…


(Michal Bultrowicz) #2

A question for the guests. You didn’t go into more detail about determining how exactly
do you know that the insight coming out of a model or a data set is wrong,
other than the subjective judgment of the researcher.

And that’s something I’d like to know more about, since I haven’t seen it explained
a lot. Getting rid of biases seems undeniably good if they make the system
perform worse. But isn’t deciding about what is biased/inaccurate based on personal
opinions also biased and inaccurate?

Let’s say that we come up with research that perpetuates some stereotype.
It’s determining the validity of that finding an issue of gaining more data
or doing more statistical analysis, not whether the researcher agrees with the results?
Science and truth are impersonal, right? So where exactly is the place for ethics in here,
which so far don’t seem to be one of humanity’s solved problems? :slight_smile: