machine learning disasters
I don't know, there might be disasters which happened due to ML but my guess is that it might be hard to really consider ML as the cause: ML is always deployed in a setting designed by humans for a very specific task, and the design must take into account the statistical nature of ML. So I would tend to interpret most problems as caused by a wrong design, not by ML itself.
If you are looking more generally into problems in the applications of ML, as far as I know the most common problems cited are:

Increasing existing bias, for instance face recognition being is more accurate with white than black faces.
Lack of transparency, especially in critical applications like medicine, military, law enforcement
Lack of a legal responsibility framework, e.g. who is at fault if a self-driving car causes an accident?