>I'm not sure why those old theories needed such debunking.
Well, in case you actually "dr", it's because those old theories not only are still held by many, but are also re-surfacing in the form of superficial deep learning applications -- as the article mentions.
To add to that, I guess the intent of the writers is explained by the following:
> We expect that more research will appear in the coming years that has similar biases, oversights, and false claims to scientific objectivity in order to “launder” human prejudice and discrimination.
Historical theories are brought up (1) to show that they are resurfacing and (2) because sciences (and I'm inclined to say DL in particular) are susceptible for making similar mistakes. The hard part is that these biases are often not clear at all, as they are based on general preconceptions/stereotypes and the theories thus confirm something we think we know (confirmation bias).
For example, I have been examining emotion recognition software [1] with which, just as with physiognomy, the face is taken as a proxy for a person's mental state. Just as OP examines the terms "criminal" and "justice" one could inquiry into the concepts underlying the digitization of emotions, such as "anger" and "joy". Terms that seem very clear on a brief encounter, but when further examining them turn out to be heavily influenced by eg. culture. Though not as obviously poignant as incriminating an innocent person, one should still wonder then what it means to feel 34% angry.
Now, this is a single example, but I guess OP's use of historical theories allows for a critical look at more DL applications out there. And maybe helps convince laymen (policy makers that buy and employ such technology) that DL is not an easy answer to complex social/political problems, such as OP's example of Faception's classifiers for terrorists, paedophiles and white-collar offenders.
Well, in case you actually "dr", it's because those old theories not only are still held by many, but are also re-surfacing in the form of superficial deep learning applications -- as the article mentions.