arXiv:2212.04382v5 Announce Type: replace-cross Abstract: For a Bayes classifier whose input space is a graph, we study the structure of the boundary, which comprises those points for which at least one neighbor is classified differently. The scientific setting is assignment of DNA reads produced by next generations sequencers to candidate source genomes. We show that the boundary is both large and complicated in structure. A new measure of uncertainty, Neighbor Similarity, which compares the classifier result for an input point to the distribution of results for its neighbors, not only tracks
The proliferation of AI systems across critical applications necessitates a deeper understanding of their decision-making boundaries and uncertainties, particularly as regulatory scrutiny increases.
This research provides fundamental insights into classifier robustness and interpretability, crucial for deploying reliable AI in sensitive domains like genomics and potentially broader scientific and industrial applications.
The introduction of 'Neighbor Similarity' offers a new metric for uncertainty, potentially leading to more transparent and reliable AI systems by better characterizing classification boundaries.
- · AI developers
- · Bioinformatics researchers
- · Regulators of AI systems
- · Genomic sequencing industry
- · AI systems lacking interpretability
- · Applications with high-stakes classification errors
Improved methods for evaluating and ensuring the safety and reliability of machine learning classifiers will emerge.
Increased trust and adoption of AI in fields requiring high accuracy and explainability, such as healthcare and scientific research.
New standards and regulatory frameworks may incorporate metrics like Neighbor Similarity to assess AI robustness and uncertainty.
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Read at arXiv cs.LG