arXiv:2606.17646v1 Announce Type: cross Abstract: Saliency map visualizations explain image-based AI predictions by pointing to regions, but these are often unintuitive and semantically unclear, leaving an interpretability gap. We argue that AI explanations should be intuitive -- coherent to user knowledge, yet simple and selective to accelerate interpretation. Inspired by artistic drawings, we propose SketchXplain to generate sketch-based visual explanations for intuitive image-based explainable AI (XAI). Combining techniques in saliency maps, concept-bottleneck models, and sketch optimizatio
The proliferation of AI systems across critical applications necessitates more intuitive and trustworthy explanations to facilitate adoption and oversight.
Improving the interpretability of complex AI models is crucial for building trust, enabling debugging, and ensuring ethical deployment in high-stakes environments.
Traditional opaque saliency maps are being augmented or replaced by more human-centric, sketch-based explanations, making AI decisions more accessible to non-experts.
- · AI ethicists and regulators
- · Developers of AI-driven critical systems
- · Users of complex AI applications
- · Explainable AI (XAI) research community
- · Companies with opaque 'black box' AI products
- · AI systems lacking interpretability features
AI developers will increasingly integrate intuitive explanation methods like SketchXplain into their products to meet user and regulatory demands.
Greater interpretability will accelerate the adoption of AI in sensitive fields such as healthcare and law, where understanding decision rationale is paramount.
Improved explainability could foster a societal shift towards greater trust in AI systems, potentially influencing public policy and education around AI literacy.
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Read at arXiv cs.AI