arXiv:2606.00011v1 Announce Type: cross Abstract: Despite the promise of AI to assist complex decisions, practitioners still lack ways to detect likely failures and inspect the consequences of model edits before committing them. We present RuleEdit, an interactive, rule-guided human-AI model editing system that (i) surfaces likely failures through interpretable mismatch signals from rule tables and (ii) supports user-authored rule feedback with prospective previews of projected performance changes and embedding shifts. We instantiate RuleEdit in stroke rehabilitation assessment and evaluate it
The increasing complexity and opacity of AI models necessitate improved human-AI collaboration tools for safe and effective deployment, particularly in critical applications.
This development allows practitioners to proactively identify and mitigate AI model failures, enabling more trustworthy and widespread AI adoption in high-stakes environments.
The ability to preview the impact of AI model edits fundamentally shifts how AI systems are developed and validated, moving towards more iterative and failure-guided approaches.
- · AI developers
- · Healthcare sector
- · High-stakes decision-making industries
- · Human-AI interaction researchers
- · Opaque AI systems
- · Purely black-box AI validation methods
Improved reliability and explainability of AI applications in sensitive domains like healthcare.
Accelerated integration of AI into regulated industries, leading to new compliance standards for human-AI oversight.
Increased demand for explainable AI tools and human-AI feedback loops across all enterprise AI deployments.
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Read at arXiv cs.LG