ai and brittle systems

NinjaAI.com

AI systems exhibit brittleness when they perform reliably under narrow conditions but fail dramatically with slight input changes or edge cases. This fragility arises from over-reliance on training data patterns rather than robust understanding. Mitigation strategies focus on diverse training, adversarial defenses, and hybrid human-AI approaches.milvus+1​

Brittleness describes AI's sharp performance drop outside trained scenarios, such as image classifiers failing on rotated or blurry inputs. Machine learning models learn correlations, not principles, leading to issues like perceptual failures in autonomous vehicles under weather variations or occlusions. Rule-based systems falter on unforeseen inputs, while neural networks suffer from overfitting to biased data.rckennedysc+4​

• Limited generalization from narrow datasets, causing failures in real-world variability like accents in speech recognition.linkedin+1​

• Data biases amplifying unfair predictions, such as in hiring algorithms.texta​

• Overfitting, where models memorize instead of understanding core concepts.linkedin​

Adversarial examples—subtle perturbations invisible to humans—trick models into misclassifications, like altering a stop sign to appear as a speed limit. These exploit neural network sensitivities, posing risks in security and autonomous systems. Defenses include robust training on diverse data and perturbation detection.zilliz+4​

Diverse, inclusive datasets improve adaptability, while frameworks like NIST AI RMF address brittleness as a technical risk. Hybrid systems combining AI with human oversight enhance resilience in safety-critical applications. Ongoing research emphasizes out-of-distribution testing for certification.arxiv+3​

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18. https://ui.adsabs.harvard.edu/abs/arXiv:2009.00802
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Defining BrittlenessKey CausesAdversarial VulnerabilitiesMitigation Strategies