Tucker, Jason | 2026
Engineering Psychology and Cognitive Ergonomics. Conference paper
Dominant approaches to explainability in AI emphasise post hoc technical transparency, overlooking the socio-technical contexts in which systems are developed, deployed, and experienced. This paper argues that beginning AI adoption processes with Question Zero (Q0), “Should we adopt an AI system in the first place?”, reframes explainability as an essential requirement across the entire AI lifecycle rather than a narrow compliance task. Q0 challenges entrenched techno-solutionist assumptions that position AI as the default or best option, encouraging early integration of considerations of explainability in system design. By foregrounding this, Q0 shifts explainability towards purpose aligned, stakeholder aware forms that move beyond generic model centred outputs. Further, the paper reflects on how explainability can also strengthen Q0 by providing tools to assess the proportionality of AI adoption, clarify problem framing, and make visible the alternatives excluded during design. Taken together, these dual perspectives, Q0 for explainability and explainability for Q0, offer multidimensional opportunities for enhancing explainability. The paper illustrates this argument through reflection on the need, and value of, applying QO in the context of the European Commission’s Apply AI Strategy.
