Critics rightfully identify that AI models are biased against marginalised groups. These biases deteriorate our shared hermeneutical resources—the narratives, frameworks, and concepts that structure how we understand the world and ourselves—by reflecting and exacerbating existing oppressive narratives. However, this is not the only way that AI models are sources of hermeneutical impoverishment. I propose that AI models warp our hermeneutical resources, not only by reinforcing existing problematic representations of identity groups, but by changing how these groups are represented. That is, AI models are conceptual engineers, capable of revising our social concepts. When certain deep machine learning models perform predictions, they construct social concepts. Crucially, these algorithmic concepts differ from their human-constructed counterparts due to unavoidable trade-offs in model development. In constructing revised algorithmic concepts, AI models act as conceptual engineers. Once introduced, algorithmic concepts can take the place of our own concepts. Through these hermeneutical changes, AI models can also make a difference to our underlying social ontology: in redefining how we think of ourselves, they can redefine who we are. Finally, I offer upshots of attending to AI models as novel sources of epistemic and ontological harm.
