Warmhold Jan Thomas Mollema

Warmhold Jan Thomas Mollema

Epistemic injustice related to AI is a growing concern. In relation to machine learning models, epistemic injustice can have a diverse range of sources, ranging from epistemic opacity, the discriminatory automation of testimonial prejudice, and the distortion of human beliefs via generative AI's hallucinations to the exclusion of the global South in global AI governance, the execution of bureaucratic violence via algorithmic systems, and interactions with conversational artificial agents. Based on a proposed general taxonomy of epistemic injustice, this paper first sketches a taxonomy of the types of epistemic injustice in the context of AI, relying on the work of scholars from the fields of philosophy of technology, political philosophy and social epistemology. Secondly, an additional conceptualization on epistemic injustice in the context of AI is provided: generative hermeneutical erasure. I argue that this injustice the automation of 'epistemicide', the injustice done to epistemic agents in their capacity for collective sense-making through the suppression of difference in epistemology and conceptualization by LLMs. AI systems' 'view from nowhere' epistemically inferiorizes non-Western epistemologies and thereby contributes to the erosion of their epistemic particulars, gradually contributing to hermeneutical erasure. This work's relevance lies in proposal of a taxonomy that allows epistemic injustices to be mapped in the AI domain and the proposal of a novel form of AI-related epistemic injustice.

Warmhold Jan Thomas Mollema, Thomas Wachter

Given the massive integration of AI technologies into our daily lives, AI-related concepts are being used to metaphorically compare AI systems with human behaviour and/or cognitive abilities like language acquisition. Rightfully, the epistemic success of these metaphorical comparisons should be debated. Against the backdrop of the conflicting positions of the 'computational' and 'meat' chauvinisms, we ask: can the conceptual constellation of the computational and AI be applied to the human domain and what does it mean to do so? What is one doing when the conceptual constellations of AI in particular are used in this fashion? Rooted in a Wittgensteinian view of concepts and language-use, we consider two possible answers and pit them against each other: either these examples are conceptual metaphors, or they are attempts at conceptual engineering. We argue that they are conceptual metaphors, but that (1) this position is unaware of its own epistemological contingency, and (2) it risks committing the ''map-territory fallacy''. Down at the conceptual foundations of computation, (3) it most importantly is a misleading 'double metaphor' because of the metaphorical connection between human psychology and computation. In response to the shortcomings of this projected conceptual organisation of AI onto the human domain, we argue that there is a semantic catch. The perspective of the conceptual metaphors shows avenues for forms of conceptual engineering. If this methodology's criteria are met, the fallacies and epistemic shortcomings related to the conceptual metaphor view can be bypassed. At its best, the cross-pollution of the human and AI conceptual domains is one that prompts us to reflect anew on how the boundaries of our current concepts serve us and how they could be approved.