These issues are already in existence. Given ML systems are being used to filter resumes, decide whether someone should get a loan (and how much their interest rate should be), and find matches on dating services, the fact that ML systems may inadvertently feature racial or gender bias is hugely disturbing. This is likely to only get worse over time as more and more systems feature ML components.
As a simple example, word vectors are used by just about every deep learning system and recent research has found gender and racial stereotypes strongly embedded in them[3]. There's very confusing debate as to what this implies for the data, the models, and predictions[4]... :S
father:doctor :: mother:nurse, man:programmer :: woman:housemaker
black_male>assaulted, whilte_male>entitled_to
I wish I was making those examples up but they're straight out of their analysis. They also only focus on gender stereotypes but you can imagine how many similar issues might be hidden away just beneath the surface.
Even if OpenAI indicated they were explicitly interested in that direction, which afaik they haven't, it's still an area that should be of interest to the field broadly. OpenAI is still a small team and I'm certain they'd appreciate the help, especially as they already have a lot on their plate :)
A disaster scenario for me is to have ML systems help reinforce the negative aspects of our society, conveniently hidden in a black box which can never be properly inspected.
[1]: I'm primarily running off what I've seen in the recent past and their technical goals that they recently published at https://openai.com/blog/openai-technical-goals/
[3]: "Quantifying and Reducing Stereotypes in Word Embeddings" - https://arxiv.org/abs/1606.06121
[4]: https://twitter.com/jackclarksf/status/746039805595762688