In 2016, I posted about ethical dilemmas in the practice of data science, based on a talk I gave at the time. That post continues to be viewed each year so some readers must have found it useful.
The post covered several realistic case studies of ethical dilemmas I have seen in industry. Since ethics is subjective, I don't recommend any course of action. These case studies were designed to be useful in courses on data science ethics. It encourages students to debate both sides of the dilemma, and perhaps for the class to vote on a course of action.
Today's post is an update to the 2016 post. I present a set of new case studies. After each case study, I have activated a poll. You can all vote for one of the options, I will post results here once we have a good sample size.
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Thank you for participating!
To see the original set of case studies, click here.
P.S. [8-21-2023] A twitter reader pointed out that the last question should be phrased as "Should" not "Does". I definitely meant "should". The question isn't about what the laws currently say but what we think they should say.
So, why are these ethical dilemmas IN data science?
I would see them as ethical dilemmas for users(!) of data science
Posted by: gg | 08/20/2023 at 02:02 PM
gg: It is both. In all cases, data scientists are involved in the production of the technologies, and have influence on the direction of these products.
As a consumer of data science products, I have made choices to not use some products on ethical grounds.
Posted by: Kaiser | 08/21/2023 at 11:14 AM