I'm not sure "unpredictable" is quite the right term but maybe?
The problem is when you have very high impact events that are very low probability, the probability assigned to the event in the individual case (a factory explosion) is essentially zero, even though in aggregate at the group level you know these occur. Moreover, these very low-base rate probabilities are difficult to estimate accurately for various reasons, so the probability in a given case might actually be 10^-4 or 10^-100. So you end up deciding something probably won't happen, but at a societal level it will, because the probabilities are underestimated and there's lots of cases like this.
I think they'd say variance is relevant, but at the level of actual model uncertainty attached to an estimated probability or something?
The stats is a little underdeveloped, but the papers I've seen often are, except in bayesian low-base rate modeling papers, which tend to ignore the individual versus group prediction, model uncertainty, and utility combination.
In terms of the relationship between the agent and the potentially affected, neither empathy or group identity is mentioned in the paper, but that would be interesting to look at elsewhere. Hypothetically, at what point does "the other" that you treating anonymously and categorically become specific and individual to you?
After a skim, I will say I don't understand why predictable or unpredictable does not explicitly address variance. That's my confusion.
The point may be how we differently value lives with or without faces. With reference to Schelling: "In many situations, people are less inclined to bear a particular cost or exert a particular effort to protect statistical lives than to protect the same number of identified lives."