Where's the ethics?
Alongside courses and research at Georgetown, I've been working through some Coursera courses this semester—Machine Learning and Structuring Machine Learning Projects. Both are taught by Andrew Ng, a very fancy guy in this space (and the co-founder of Coursera).
They're great. Ng is a terrific teacher and makes the content relatively accessible. Plus, you get to put things you learn into practice.
Still, I'm torn about them. It's a bit worrisome that all of this computational power is being put into the hands of people around the world for free without adequate context about broader research principles or responsible implementation.
The Machine Learning course dives right into the math and its implementation, which is fine to an extent. I remembered linear algebra and my stats.
But this course misses an opportunity to incorporate an ethical and research framework into the flow. These topics don't need to be introduced during the first lecture, but certainly in early lectures and carried throughout.
Some of this might be covered in the week on advice for system design and applications. I haven't gotten to that yet and will update when I do.
But this framing is certainly not the tenor of the Structuring Machine Learning Projects course. That is also framed more in terms of technical skill—how to set up projects, find errors and fix them, and not waste time down rabbit holes, for instance.
These courses are popular: 70,834 people took the time to rate the Machine Learning course, and it currently has 4.9 out of 5 stars.
You might lose some of that audience if you incorporate broader research principles into these technical courses. And doing so may not shape all minds. But it's still worth it, not least to send a signal.