The debate regarding the FFA pertain to whether it is the only region or even a critical region that does face processing. Some labs have shown that face processing information can be found in other regions of the brain that are not the FFA. Yet some labs have shown that the FFA is recruited to process fine levels of category distinctions. For example, bird and car experts have been shown to engage some level of FFA activity when processing these stimuli compared to novices. These findings suggest that the FFA is not processing faces per se, but visual representations that have come to require high-levels of fine discrimination through experience, of which faces are the best example of this currently.
I suggest that a more flexible definition is called for when thinking about the FFA and its role in processing visual information. Certainly, it does seem that faces occupy a special place in human experiences. On the other hand, it is difficult to explain why there would be a brain region that codes for faces and faces along based simply based on genetic or biologically determined causes.
In terms of a neural network, if indeed the brain consists of many different sub-types of neural networks that conglomerate to form one large complex network, the FFA is a sub-network specialized to perform a specific operation that is maximized and specialized (trained) for a specific information domain - faces. This or these specific operation(s) could involve identification, discrimination, recognition, or all of these, or even a yet unknown operation. Certainly neural network non-linearities can surprise us! Moreover, these operations have been tuned for a specialized class of stimuli that consists of eyes, nose, mouths, and other visual characteristics of faces when occurring together as a whole (whether from external input, or through internal imagination or retrieval).
What this means is that if you were able to "remove" the FFA, and plug it into a computer so that you can feed this FFA network with inputs and measure its outputs, you could theoretically feed it anything, but the information would be most meaningful or organized when the inputs correspond to information about a face. Of course, this would require us to know what is the language of the input to perform such an experiment.
Other types of inputs may elicit some level of meaningful output of the FFA. Neural network do that. Yet other types may elicit nothing at all. This does not necessarily mean that the FFA outputs from such inputs is useless, nor does necessarily mean that it is used! It is just output. What higher-level brain mechanisms do with the output depends on the task, and how the brain is wired to treat outputs from its sub-networks. It may be ignored, or it may actually incorporate relevant information. That is, the FFA is agnostic to the incoming information. It does not care. It will process it anyway. But other regions decide whether what is it saying needs to be incorporated or not, or if it should be further modified even.
Such a view would reconcile why the FFA is special for faces, yet seems to be carrying some information about other stimuli. It would also be consistent with the idea that information about faces is certainly also available to a certain extent in non-FFA regions, the same principles being applied to these other sub-networks. It would also be consistent with how self-organizing behavior in neural network (see von der Malsburg article [link]) can lead to a consistent topology across every person that processes a particular stimulus in a particular way in a particular spatial location.
This is probably not a new idea, but needs to be clarified in the literature I think.