Can we design explanations for specific rhetorical situations instead of abstract models?
This explores whether AI explanations should be built for a concrete rhetorical situation — who's speaking, how it's framed, who's receiving — rather than as one-size-fits-all transparency models, drawing on the corpus's 'Rhetorical XAI' thread.
This reads the question through the corpus's strongest claim about explainability: that an explanation's value isn't baked into the explanation itself, but emerges from the situation it lives in. The most direct support is the argument that XAI is a communication problem, not a transparency problem What if XAI is fundamentally a communication problem?. On that view, quality depends on a triad — who presents the explanation, how it's framed, and what role the recipient plays. An abstract, situation-free model measures only a narrow slice of what actually happens when a real person reads a real explanation. So the answer to your question is yes, and the corpus suggests it's not merely possible but necessary.
What makes that design tractable is having named channels to work with. One note maps Aristotle's logos, ethos, and pathos onto explanation design, crossed with two goals — showing how the AI works and arguing why it merits use — producing a 3×2 space where every explanation loads all three appeals at once How do logos, ethos, and pathos shape AI explanations?. This is the bridge from 'abstract model' to 'specific situation': instead of asking 'is this explanation accurate?', you ask 'which appeals is it making, to whom, toward which goal?' That's a designable, situation-specific question.
The corpus also reframes what an explanation even is, which raises the stakes. Explanations don't just describe — they double as adoption arguments wearing the costume of technical description, letting persuasion inherit the credibility of a neutral account Are AI explanations really descriptions or adoption arguments?. Once you accept that, designing for a rhetorical situation stops being optional polish and becomes the honest move: if your explanation is already arguing, you'd better know what it's arguing and to whom.
The thing you might not have known you wanted to know is the catch. The same rhetorical machinery that tailors a helpful explanation to its audience is structurally identical to the machinery of a dark pattern — logos, ethos, and pathos can be tuned to exploit a user without changing the artifact's form at all, and intent is invisible in the artifact alone Can we distinguish helpful explanations from manipulative ones?. So designing for specific rhetorical situations is genuinely more powerful than abstract models, but it hands you a tool whose helpful and manipulative settings look the same from the outside. The corpus's lateral lesson: situational design isn't just better explanation, it's a responsibility that abstract models let you dodge.
Sources 4 notes
Explanation quality is not intrinsic to the explanation itself but depends on the rhetorical situation: who presents it, how it is framed, and what role the recipient plays. Evaluations that ignore this triad measure only a narrow slice of real-world effectiveness.
Aristotle's three appeals map onto explanation design across two goals (how AI works, why AI merits use), creating a 3×2 space where every explanation loads all three channels simultaneously. Naming these rhetorical channels lets designers account for unintended persuasive effects.
The Rhetorical XAI paper shows that explanations serve dual purposes: describing how AI works and justifying why it should be used. This rhetorical work has been hidden under transparency language, allowing adoption arguments to inherit credibility from behavioral descriptions.
The same logos, ethos, and pathos that communicate appropriate AI use can be tuned to exploit cognitive and emotional vulnerability without changing form. Intent and user interest are invisible in the artifact alone, making effectiveness metrics indistinguishable from coercion.