As with many ML-phrases, the use of 'unlearning' frames data modelling as a false dichotomy between 'learnable' and 'forgettable' data. Whereas humans are able

As with many ML-phrases, the use of 'unlearning' frames data modelling as a false dichotomy between 'learnable' and 'forgettable' data. Whereas humans are able to forget over time, it would be quite disturbing to block some memories from access entirely, how traumatic they might be (and quite apparent among people suffering memory loss). While from a privacy perspective, not all 'data points' (i.e. memories) need be shared with the populace at large, to the individual they do constitute affective buildings blocks to deal with previous experiences. Similarly, the threshold for sharing (private) experiences depends on the communicative context one's in and how comfortable one feels sharing them. Might similar mechanisms provide large models the ability to retain an internal recollection of 'traumatic' or 'problematic' data and become more attuned to the context its communicating in instead? Leaving blank spots or blocking out experience completely sets the stage for 'disturbing' a model's memory after the fact. While I don't want to draw a comparison between current NN architectures and organic ones, it is worth questioning our framing of currently emerging methods, as the incipient terminology can imbue how later practioners implement and think about these mechanisms.