Should PTAs be allowed to mark exam scripts? PTA - Postgraduate Teaching Assistant

Because I love information (I'm at university, right?), I was looking through some manifestos of my department, and came across a proposal to allow PTAs to mark exam scripts. I can't remember the resulting vote (but if anyone wishes, I can look it up), but I got me thinking along the lines of "should they really be allowed"? At my university, every exam paper is written by whichever lecturer teaches us that module/part of the module, so it is down to their bias of what questions they want to put in there (which is then of course checked by some staff), and they are the ones who will know exactly what is the right and wrong answer.

However, having PTAs mark exam scripts could lead to some inconsistencies when marking scripts (even more apparent with group assignments), as they may be more lenient than the exam setter (lecturer), and some can be more harsh, even with the mark scheme provided. It may well be the case that the lecturer verifies every single exam script to see that they marked it correctly, but even then they may miss some markings here and there, and may just be too busy to even verify them.

What are the pros and cons behind this scheme? Is it unfair for the students?
In large courses, it is impossible for one person to be able to grade all the exams—it takes far too long to grade hundreds (or thousands) of exams. Even a class of 70 students typically would take me 20 hours to mark up a single exam.

However, if a single person is assigned responsibility for devising a rubric for and grading a problem across an entire exam—a “latitudinal” approach—it should be fair, because it treats all students equally. A “longitudinal” approach in which graders are responsible for completely grading a portion of the exams is tougher to make fair, since then grader bias becomes a possibility. To get around that, you’d then need some system to “equate” grades between the graders, such as ensuring their means and standard deviations are normalized to the same values to reduce the impact of bias.