That said, that doesn't mean closed-source is any better (a different tradeoff), but it would be foolish to think that open-source software is not being exploited for its open-source properties.
I can't speak for Kilobug, but my answers would be:
1. It depends on your values. E.g., how much do you value your own welfare compared to family, friends, co-workers, fellow citizens, and those other people? If you want to be conscious about it, you need to think about what you value and how you might have done things differently in that light.
2. I probably thought I was I a deotonologist, but if you carefully study your own and other people's decisions, the vast majority are consequentialists with values that tend to selfishness. WItness how many Americans are angry about the Central American children/teenagers trying to get into the US.
3. As others have commented, doing a full analysis is time-consuming and uncertain (hence "maximum expected utility"). Most of the time, one has to follow rules that generally (so one believes) that have good consequences. And generally, virtue and duty are good rules. But people make up all sorts of rules with little sense behind them. My grandmother thought opening an umbrella indoors was bad luck, but I am a little skeptical about that one.
Introduction to Algorithms by Cormen et al.
A good statistics book. Mine is an old thing: Mathematical Statistics with Applications by Mendenhall and Scheaffer.
A good operations research book (linear programming, queueing theory, Markov models/decision processes, and the like). Another old thing: Operations Research by Hillier and Lieberman.
Other than that, it's books that are/were used often for programming reference: Common Lisp: The Language by Steele and LaTeX: A Document Preparation System by Lamport look the most worn.
Hopefully, someone will come up with something a little more recent than the "old things" I mentioned above.
Instead, figure out (or estimate) what percentage of the shares are owned by US residents. Multiply that percentage times the corporation's profit times the corporate tax rate and that is what they should pay.
Note: Any public corporation knows who are the immediate owners, so that they can send out shareholder info. However, a shareholder might be another corporation which is owned by other corporations, etc. Hence, the need to estimate (along with following the money as much as possible).
It goes into a lot of detail on how Snowden first attempted to contact Glenn Greenwald (who couldn't use secure communication at first) and then contacted Laura Poitras who was making a documentary about security. Lots of detail about their getting together, vetting each other, and personal threats to Greenwald and Poitras (as well as Snowden) as well as a good timeline of how events unfolded.
After reading this article I am more concerned than ever about the extent of US surveillance and the extent to which the USG will go to suppress information and intimidate whistle-blowers. Good to see that the NYTimes finally publish some real journalism on this subject.
Also... accompanying transcript of "Q&A — Edward Snowden" http://www.nytimes.com/2013/08/18/magazine/snowden-maass-transcript.html"
Link to Original Source
The results of the ML algorithm need to be cleaned up and organized so that they can be used by the rest of the system.
No one (currently) can tell you which ML algorithm will work best on your problem and how its parameters should be chosen without a lot of study. Preconceived bias (e.g., that it should be biologically based, blah, blah) can be a killer here.
The best results typically come from combinations of ML algorithms through some kind of ensemble learning, so now your have the problem of choosing a good combination and choosing a lot more parameters.
All of the above need to work together in concert.
Certainly, it's not a bad idea to try to make this process better, but I wouldn't be expecting miracles too soon.
I suppose many might say it's not computer science without the math, but you can still teach much about computer technology and software design while being gentler with the math.
Personally, I've enjoyed the courses because I like math (except the quantum computation course, which was dreadful), but I know most of our CS students would be buried by the math. For the record, I'm at a state univ with some good research, but nowhere near a flagship. We do want to graduate some students, and the students we do graduate are in demand in our area.