Is anyone working on safe selection pressure for digital minds?

Back in the 1990s, some of us were working on using genetic algorithms (simulated evolutionary methods) to evolve neural network architectures. This was during one of the AI winters, between the late 1980s flurry of neural network research based on back-propagation, and the early 2000s rise of deep learning in much larger networks. 

Some examples of this work are here (designing neural networks with genetic algorithms, 1989), here (genetic algorithms for autonomous robot control systems, 1994), here (artificial evolution as a path towards AI, 1997), and here (technological evolution through genetic algorithms, 2000). I'm citing mostly work I did at Stanford with Peter Todd and with the cognitive science group at U. Sussex (UK), such as Dave Cliff.

There was a lot of other similar work at the time in the research areas of genetic algorithms, artificial life, autonomous agents, and evolutionary robotics, published in conference proceedings with those kinds of titles and key words.

Most of this work didn't directly address AI safety issues, however, such as safe selection pressures for digital minds. But it might be of interest for some historical context around these issues. And the work did try to make some connections between neural networks, simulated evolution, autonomous agents, cognitive evolution, and evolutionary psychology.

I recommend <The Age of EM> by Robin Hanson. Although it deals with realistic predictions rather than normative facts, it is worth considering.

You might find it worthwhile to read https://www.nickbostrom.com/propositions.pdf

More generally, there are people working in the AI welfare/rights/etc space. For instance, Rob Long.

And if no one is working on it, is there an organisation that would be interested in starting working on it?

I don't currently understand what area of work you're trying to point out with this question. You might want to be more specific to get good answers.

Here are some different things which you might be trying to talk about:

1. From a philosophical perspective, when is ML itself unethical due to effectively causing the death of some agent? (Or replace ML with other selection techniques.)
2. If we have economic selection pressures over digital minds/AIs what sorts of predictable problematic outcomes result?
3. If we select ML systems to achieve good results according to a lossy reward signal we might run into issue (e.g. reward hacking), what can we do to resolve this?

For (2) you might be interested in the sort of discussion in "Age of EM", though I expect that the situation is pretty different in the de novo AI case.