I do not think you should be worrying about superintelligence. If you are investing in AI firms that are going to make products that will be profitable, providi

I do not think you should be worrying about superintelligence. If you are investing in AI firms that are going to make products that will be profitable, providing a return on investment, they should focus on making the products, not on making general AI. (I mean read the book "Superintelligence" if you want, it's an interesting read, and may help you have conversations with certain people, but as someone else here said, it's an "interesting distraction". There are more and more zealous true believers in the tech world, unfortunately; some of your investees may bring it up themselves!) There are real ethical problems in systems that learn from data. I'm describing them that way rather than as "AI", because in fact these problems can show up anywhere along the spectrum from deep neural networks all the way down to the simplest linear models. These can arise from biases in the training data or in the learning process. Others have posted Google's AI principles, which are good. The keyword for researchers working in this area is FAT -- Fairness, Accountability, Transparency. There are conferences [1] in this area that have a lot of good work. You may also want to see the syllabus for Moritz Hardt's course [2]. I would also add -- we're in an unsettling place right now. It's not clear that there are "best practices" that can be widely recommended. Many proposed definitions of fairness are inadequate or mutually contradictory. [1]: https://fatconference.org/2018/index.html https://fatconference.org/2018/index.html [1]: http://www.fatml.org/schedule/2018 http://www.fatml.org/schedule/2018 [2]: https://fairmlclass.github.io/ https://fairmlclass.github.io/