Reliability, Faithfulness, and the Limits of Post-hoc Explanations of Opaque Scientific Models
arXiv:2606.29346v1 Announce Type: new Abstract: Post-hoc explanation methods are routinely used to interpret scientific machine learning models, with the deliverable understood to be insight into the phenomenon the model has been trained on. The transition may be taken to be secured once the model is reliable enough and the explanation faithful enough. We argue it is not. Reliability checks that the model's predictions match the phenomenon's outcomes, and faithfulness checks that the explanation matches the model, but neither checks whether the model works as the phenomenon works, which is wha
The proliferation of complex AI models, particularly in scientific research, makes the challenge of their interpretability increasingly pressing and central to their adoption and trustworthiness.
This research highlights a fundamental limitation in validating AI models intended for scientific discovery, suggesting that current explanation methods may not provide true mechanistic understanding.
The accepted metrics for evaluating AI model explanations (reliability and faithfulness) are shown to be insufficient for ensuring genuine scientific insight or causality.
- · Fundamental AI interpretability researchers
- · Scientists developing transparent AI models
- · Developers of intrinsically interpretable AI architectures
- · Developers relying solely on post-hoc explanation methods
- · Applications requiring true mechanistic understanding from opaque models
- · Policymakers making decisions based on unvalidated AI explanations
Increased scrutiny and skepticism of AI applications in high-stakes scientific and industrial domains where interpretability is crucial.
A push towards developing 'explainable by design' AI models rather than relying on after-the-fact explanations.
Potential slowdown or redirection of AI adoption in fields demanding deep causal understanding, until novel interpretability paradigms emerge.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG