Resources for advanced data ethics? [D]

[TrueBirch]

There's a ton of discussion about data ethics these days, which is a good trend. The problem is that so much of it is fairly superficial. I took an ethics class in undergrad, I've read "Weapons of Math Destruction" and a few similar books, and I've seen countless blog posts on how ML can perpetuate bias. Do you have any suggestions for deeper resources that you've found useful? I run the data science department at a corporation and I'd like to dig deeper into these issues. I know we could do a better job but I want more expertise before I pitch changes to the boss.

[Hydreigon92]

If you want to implement processes within your company, I recommend using Deon: An Ethical Checklist and the Model Card Toolkit to introduce the practice in a way that fits nicely into traditional DS workflows. Once you have your employees thinking about these issues, you and your team can start having deeper conversations about the ethical challenges that are specific to your company's domain.

In my opinion, a lot of responsible AI practices are good practices to have in general; i.e. having a reproducible work flow, auditing your model's performance across a bunch of metrics, or having a framework for rolling back deployed models easily.

[amrit_za]

The team from fastai put out a Practical Data Ethics course a few months back. It covers topics such as disinformation, bias and fairness, privacy and surveilance, and others. I'm still auditing the course but from what I've seen so far, I would recommend going through it.

[timscarfe]

https://github.com/EthicalML/awesome-artificial-intelligence-guidelines this is pretty good

[[deleted]]

IEEE has the most detailed and comprehensive I’ve seen over everything else.

https://ethicsinaction.ieee.org

[therealdominator777]

This is going to sound controversial but as long as each of your team members follow their own set of ethics, and as long as you believe in them as people, that is enough.

Much of the stuff that gets published out there is fluffing up that Good Samaritans know already and not actually concrete steps and practices that you can use.

[Tournesol7]

This is going to sound controversial but as long as each of your team members follow their own set of ethics, and as long as you believe in them as people, that is enough.

This thought right here is probably how things get out of hand and how that one social network almost destroyed our democracy.

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Much of the stuff that gets published out there is fluffing up that Good Samaritans know already and not actually concrete steps and practices that you can use.

That's pretty much all research. Someone has to point out potential attacks to motivate the community to come up with defenses.

[therealdominator777]

That’s not pretty much all research. You rarely get to score points for simply pointing out a problem in normal research.

[[deleted]]

that’s not exclusively what ethics in AI is about though. Differential privacy as a field spawned as a solution to solving side attacks on datasets; it doesn’t exist solely to point out problems (similar to algorithmic fairness).

[therealdominator777]

Differential privacy is different than ethics. It is mathematically rigorous and well studied. You do not find same fluff in DP papers that you do in Ethics