AI2030 Responsible AI Tools Marketplace

Key Takeaways

Explanations shape trust and adoption, not just understanding: Traditional XAI focuses on helping users understand how AI systems work, but this paper argues that explanations also function as persuasive arguments that influence whether people find AI systems credible and trustworthy enough to use in the first place.
Three design principles can make AI explanations more effective: The paper identifies three rhetorical approaches—logos (making technical logic understandable through visuals and text), ethos (building credibility through trustworthy sources and appropriate context), and pathos (connecting to users' actual needs and emotions)—as key to designing explanations that work in real-world settings.
How explanations feel matters as much as what they say: Beyond just conveying information, explanations can produce emotional and experiential effects that influence user decisions. The paper highlights that explanations need to engage users emotionally and align with their situated needs during actual interactions, not just present neutral technical facts.
Current XAI design may miss important real-world factors: Traditional XAI evaluation focuses narrowly on whether users understand system mechanics, potentially overlooking how explanations actually influence adoption decisions, appropriate trust levels, and whether people find systems genuinely useful for their specific contexts.
Bridging technical and persuasive goals requires intentional design: The paper suggests that responsible AI requires deliberately integrating both technical accuracy (logos) with credibility-building (ethos) and user-centered framing (pathos), rather than treating explanation as purely a technical problem.

Abstract

We explore potential benefits of incorporating Rhetorical Design into the design of Explainable Artificial Intelligence (XAI) systems. While XAI is traditionally framed around explaining individual predictions or overall system behavior, explanations may also function as rhetorical arguments that shape how users evaluate a system's usefulness and credibility, and how they develop appropriate trust for adoption. In real-world, in-situ interactions, explanations can thus produce experiential and affective rhetorical effects that are not fully captured by traditional XAI design goals that focus primarily on how AI works. To address this gap, we propose Rhetorical XAI, which bridges two explanatory goals: how AI works and why AI merits use. Rhetorical XAI comprises three appeals in explanation design: logos, which aligns technical logic with human reasoning through visual and textual abstractions; ethos, which establishes contextual credibility based on the explanation source and its appropriateness to the decision task; and pathos, which engages user emotionally by framing explanations around their motivations, expectations, or situated needs during interaction. We conduct a narrative review synthesizing design strategies from prior XAI work aligned with these three rhetorical appeals, highlighting both opportunities and challenges of integrating rhetorical design into XAI.

Clarify the contextual positioning of our framework in relation to prior XAI design work and expand the review details

Key Takeaways

Explanations shape trust and adoption, not just understanding: Traditional XAI focuses on helping users understand how AI systems work, but this paper argues that explanations also function as persuasive arguments that influence whether people find AI systems credible and trustworthy enough to use in the first place.
Three design principles can make AI explanations more effective: The paper identifies three rhetorical approaches—logos (making technical logic understandable through visuals and text), ethos (building credibility through trustworthy sources and appropriate context), and pathos (connecting to users' actual needs and emotions)—as key to designing explanations that work in real-world settings.
How explanations feel matters as much as what they say: Beyond just conveying information, explanations can produce emotional and experiential effects that influence user decisions. The paper highlights that explanations need to engage users emotionally and align with their situated needs during actual interactions, not just present neutral technical facts.
Current XAI design may miss important real-world factors: Traditional XAI evaluation focuses narrowly on whether users understand system mechanics, potentially overlooking how explanations actually influence adoption decisions, appropriate trust levels, and whether people find systems genuinely useful for their specific contexts.
Bridging technical and persuasive goals requires intentional design: The paper suggests that responsible AI requires deliberately integrating both technical accuracy (logos) with credibility-building (ethos) and user-centered framing (pathos), rather than treating explanation as purely a technical problem.

Abstract

Clarify the contextual positioning of our framework in relation to prior XAI design work and expand the review details

Rhetorical XAI: Explaining AI's Benefits as well as its Use via Rhetorical Design

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links

Rhetorical XAI: Explaining AI's Benefits as well as its Use via Rhetorical Design

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links