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What if XAI is fundamentally a communication problem?

Does explanation effectiveness depend on who delivers it, how it's framed, and who uses it? This challenges the dominant technical view that treats explanations as context-independent outputs.

Synthesis note · 2026-05-02 · sourced from Human Centered Design

The Rhetorical XAI paper makes the strong claim that XAI is not solely a technical problem of producing faithful rationales — it is a communication problem because explanations are situated messages whose interpretation is mediated by who presents them, how they are framed, and who must act on them. Different stakeholders use the same explanation for different goals: developers debug, ethicists assess accountability, end-users decide whether to trust an output for a specific task. The same artifact takes on different meanings across these positions, so effectiveness is not intrinsic to the explanation. It is a property of the triad — source, framing, recipient — and any evaluation that holds the recipient role constant or implicit is measuring something narrower than what the explanation actually does in deployment.

The reframing matters because the dominant XAI program treats explanation as a faithful-rationale problem and evaluates with proxies (preference, comprehension on a fixed task) that bake in a single recipient role. The communication framing forces the field to specify the rhetorical situation each explanation is built for, rather than treating "explanation" as a noun that can be optimized in the abstract. This is a Lasswell/Jakobson shift — explanation as communicative act with sender, channel, message, receiver, and code, not as interpretability output emitted from a model. Aligned with the Conversation Glossary direction: communication-centric POVs (Habermas, Goffman, Austin, Bakhtin) all start from situated messages, and rhetorical XAI is a way of importing that frame into the AI explainability literature.

This extends What makes explanations work in real conversation? from the dialogue layer up to the broader rhetorical situation: Madumal et al.'s three dimensions are the fine-grained instance of the larger source-framing-recipient claim, applied within a turn-by-turn explanatory exchange. It also runs parallel to How does AI writing escape the conversations that govern knowledge? — both insights argue that decoupling knowledge artifacts from the social processes that constitute their meaning produces an artifact that performs adequacy without delivering it. Stripping the rhetorical situation out of XAI leaves a faithful rationale that is not, for any actual recipient, an explanation.

Inquiring lines that read this note 51

This note is a source for these research framings, grouped by the broader line of inquiry each explores. Scan the bold lines of inquiry; follow any specific question forward.

Can artificial systems establish authority in domains requiring expert judgment? Can AI systems participate in genuine communication or only simulate it? What structural patterns sustain successful multi-turn dialogue and prevent breakdown? How do users confuse explanation quality with actual system accuracy? Should models ask for clarification when facing ambiguous or under-specified information? What gaps exist between benchmark performance and real deployment outcomes? How do interpretive frames override surface features in text comprehension? Can humans reliably detect and resist AI-generated misinformation? How should humans and AI agents share control and decision-making? Why do language models struggle to implement user intent accurately from prompts? Can external verification systems adequately replace learned reasoning in AI outputs? Can mechanistic interpretability methods reliably reveal what models actually know? Why does polished AI output gain credibility despite fundamental verifiability problems? Why do confident AI outputs mislead human trust calibration? What determines AI's persuasive power and how can it be detected or mitigated? How do hallucinated citations emerge in AI scholarly output? Do persona-based approaches introduce systematic biases in user simulation? How do individually-safe actions create collectively-unsafe outcomes? How do philosophical assumptions about AI consciousness affect practical harms and design? Can confidence signals reliably detect flawed reasoning in language models? Can monitoring reasoning traces and behavior detect hidden agent deception? How do models learn from self-generated outputs without cascading failures? Can readers reliably distinguish AI-written text from human writing? How can AI systems reliably guide voters without introducing political bias? How do educators verify student capability when AI can produce indistinguishable work?

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Original note title

XAI is a communication problem not a transparency problem — explanations are situated messages whose meaning depends on source, framing, and recipient role