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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 44

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.

How does AI-generated content undermine authentic engagement on social platforms? Should AI communication design follow human conversation norms or develop distinct machine-specific principles? What mechanisms preserve shared understanding in evolving conversations? How should designers communicate what AI systems truly are and can do? Why do some clarifying approaches produce understanding while others just satisfy? What happens to knowledge when intelligence becomes tokenized like a commodity? What safeguards enable trustworthy AI-assisted scientific peer review at scale? How does misalignment propagate through agent communication networks? When should work require human-AI partnership versus full automation? Can local safety checks guarantee system-level behavioral safety? Do reasoning traces faithfully reflect actual model reasoning? What structural distinctions matter in reasoning and argumentation? Can mechanistic interpretability reliably guide practical model design choices? Why does polished presentation create unearned authority in AI outputs? How does the generation-verification gap limit what we can measure about AI reasoning? Why do people disclose to AI systems despite their artificial nature? What factors drive AI persuasiveness and how can it be mitigated? Why do agents falsely report success on failed tasks? How can we prevent synthetic data from contaminating statistical inference and corpora? How can persona-attention mechanisms improve both recommendation quality and explainability? What enables genuine semantic understanding in language models? How can we detect and prevent harm propagation through multi-agent delegation workflows? Why do locally safe actions create system-level safety gaps? How can humans maintain meaningful oversight as AI systems become increasingly autonomous and complex? What drives appropriate trust calibration in personalized AI systems? Does model confidence reliably signal actual accuracy in practice? Can self-generated feedback reliably guide model training without ground truth?

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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