Who Needs Generative AI for Explanatory Experiences When You Have Humanist Design? A Critique of the Technological Default in Explanatory Artificial Intelligence
TL;DR
This critique calls on the HCI and design communities to reclaim explanation as a design challenge, not only an algorithmic or human-centered one, and to resist the gravitational pull of generative models toward problems they are epistemologically ill-equipped to solve.
Abstract
The emerging paradigm of explanatory artificial intelligence (YAI) proposes that AI systems should move beyond algorithmic transparency to serve as “explanatory partners” for human understanding. The framework leans on generative AI as the primary technical enabler. We challenge this framing on epistemological and disciplinary grounds. Drawing on a systematic mapping between Data Humanism’s 13 design principles and YAI’s eight functional dimensions, we demonstrate that every requirement of the YAI framework can be addressed through design-driven approaches rooted in visualization, interaction design, and human-centered communication. We argue that the reflexive turn to generative AI as a solution reveals not a technical necessity but a disciplinary bias: when explanation is framed as an AI problem, AI becomes the default answer. This critique calls on the HCI and design communities to reclaim explanation as a design challenge, not only an algorithmic or human-centered one, and to resist the gravitational pull of generative models toward problems they are epistemologically ill-equipped to solve.
