AI & ML
Political sense-making in "individual" human-technology-world relations: the case of LLM-generated autobiographies
arXiv cs.HC0 views
arXiv:2610.32594v1 Announce Type: new Abstract: As AI systems increasingly mediate how people describe, recognize, and present themselves, their politics cannot be reduced to what remains hidden inside the "black box." This chapter argues that AI's political significance emerges within technologically, intersubjectively, and temporally mediated interpretive practices. Bringing Arendt's political philosophy into conversation with Gadamerian hermeneutics, the chapter develops a postphenomenological relational-temporal account of political sense-making across Human-Technology-World relations. Against the charge that postphenomenology remains individualistic and using the case of LLM-generated biographies, the chapter shows how seemingly private human-LLM encounters are worldly and collective. When users ask systems such as ChatGPT to describe them, they do not simply receive algorithmic representations. They interpret, contest, edit, share, and publicly test these descriptions. In doing so, people move interpretively among affective resonance, friction, public re-articulation, and the sedimentation of meaning. This means that the so-called "micro-level" of postphenomenological analysis is already saturated with publicity: affective uptake becomes communicable, reflection becomes accountable, and uses sediment into shared standards of appearance. Simultaneously, we show that once mediation is treated as relational and temporal, the political is not an add-on at the "macro" level; it is intrinsic to the very movement through which technologies amplify and reduce, stabilize and unsettle, and thereby compose a shared world in which judgment can take place. The chapter finishes by discussing several design and policy implications for AI of such political foregrounding in postphenomenology.
Source:https://arxiv.org/abs/2610.32594PreviousFrom Perception to Pixels: Human-Derived Visually Distinct Colors for Improved Dominant Color ExtractionNextBeyond pass or fail: an information-processing account of the Turing Test
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