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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 55

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 we reliably distinguish AI-generated from human-written text? Do AI explanations represent actual model reasoning or constructed post-hoc narratives? What conversational structures and patterns determine dialogue effectiveness and coherence? What distinguishes genuine understanding from correct linguistic performance? How do dialogue structure and state management affect multi-turn reasoning quality? How do standardized forms and measurement artifacts shape observable evidence? Do benchmark score improvements actually reflect genuine AI capability gains? How do workflow position and structure enable attacks in multi-agent systems? How should control and autonomy be allocated in human-AI collaboration? Does chain-of-thought reasoning faithfully reflect actual model reasoning processes? How do mechanistic features and representations drive model behavior? What factors determine AI persuasion effectiveness and what controls mitigate it? How does tokenizing intelligence reshape labor markets and institutional outcomes? What systematic failures occur in human preference-based model evaluation? How can persona-based systems overcome their inherent limitations in modeling diversity? Why do users believe false claims despite fact-checking efforts? Can validator agreement alone ensure semantic correctness of reasoning? Does polished AI output mislead users about actual accuracy and expertise? How should AI ethics integrate consciousness, values, and transparent disclosure? How do humans calibrate and maintain trust in AI systems? How does transparency of agent reasoning affect effective oversight? What prevents or enables error accumulation in self-training systems? How has ChatGPT adoption actually reshaped labor markets and information ecosystems? Does generative AI replace coordination with individual productivity?

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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