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.
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?- Can we design explanations for specific rhetorical situations instead of abstract models?
- What makes something an addressee capable of receiving communication?
- How do dialogue dimensions predict explanation success across different exchanges?
- Why do monological explanations fail to transfer understanding compared to dialogical ones?
- How do dialogue acts and explanation moves interact to predict understanding success?
- Why might expressed satisfaction with explanations diverge from actual cognitive clarity?
- How do experts decide which information matters for a specific audience?
- Why do user studies of explanations fail to predict deployed effectiveness?
- How do organizational roles and peer interpretations shape what an explanation means?
- Why does describing a process differ fundamentally from arguing about evidence?
- Why does explanation source matter more than explanation content?
- How does the Question Under Discussion shape what content projects?
- What makes a clarifying question aligned with user interests versus structurally sound?
- How do agents distinguish between evidence framing and instruction framing in practice?
- Why does fairness depend on context and who you ask?
- Why do two experts with identical knowledge produce different outcomes in the same situation?
- What happens to knowledge production when discourse lacks social filtering?
- Can mechanistic interpretability explain explanation-execution disconnection?
- Why do feature visualizations alone fail to establish mechanistic claims?
- How do explanations borrow authority from transparency when describing adoption arguments?
- Should explanation quality be measured by user satisfaction or behavior prediction?
- How should AI explanations be evaluated as human interfaces rather than model properties?
Related concepts in this collection 3
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What makes explanations work in real conversation?
Does explanation quality depend on how dialogue partners interact—testing understanding, adjusting based on feedback, and coordinating their communicative moves—rather than just information content alone?
extends; three-dimension dialogical analysis is a fine-grained instance of the broader rhetorical-situation claim
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How does AI writing escape the conversations that govern knowledge?
If knowledge claims normally get filtered and refined through social discourse, what happens when AI generates claims outside that governing process? Why does scale matter here?
parallel reframing; both move from artifact-centric to situation-centric evaluation
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Why does rigorous-sounding AI commentary often misdiagnose how models work?
Expert commentary on AI frequently cites real research and sounds carefully reasoned, yet reaches conclusions built on unwarranted cognitive attributions. What makes this pattern so persistent in AI analysis?
same phenomenon at adjacent layer
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- Rhetorical XAI: Explaining AI’s Benefits as well as its Use via Rhetorical Design
- Expanding Explainability: Towards Social Transparency in AI systems
- Modeling the Quality of Dialogical Explanations
- LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools
- "Is ChatGPT a Better Explainer than My Professor?": Evaluating the Explanation Capabilities of LLMs in Conversation Compared to a Human Baseline
- The LLM Fallacy: Misattribution in AI-Assisted Cognitive Workflows
- Rethinking Large Language Models in Mental Health Applications
- Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?
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