arXiv:2605.27618v1 Announce Type: new Abstract: Despite the wide use of explainability techniques to attempt to understand the behavior of Artificial Intelligence (AI), the generated explanations may not always be reliable. An explanation can appear plausible to humans but fail to capture the internal reasoning of a model, particularly when dealing with complex tabular data. This paper studies the trustworthiness of local explainability techniques when applied to complex tabular classification tasks, considering evaluated metrics for three main properties: faithfulness to the model's predictio
As AI models become more complex and integrated into critical systems, the demand for trustworthy and transparent explanations of their behavior is rapidly increasing.
Reliable explainability metrics are crucial for ensuring the responsible deployment of AI, fostering trust, and preventing potential harms from opaque decision-making processes, especially in sensitive applications.
This research highlights the limitations of current explainability techniques on complex tabular data, indicating a need for more robust evaluation methods and potentially new approaches to AI transparency.
- · AI explainability researchers
- · Regulatory bodies
- · Industries deploying AI in critical applications
- · Developers of unreliable explainability tools
- · Users relying on superficial AI explanations
Increased scrutiny and demand for certified or validated AI explainability tools.
Development of standardized benchmarks and regulatory frameworks for AI explainability.
A potential slowdown in the adoption of AI in highly regulated sectors until better explainability solutions are available.
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Read at arXiv cs.LG