algorithmic brittleness
[/ˌælɡəˈrɪðmɪk ˈbrɪtəlnəs/]
nounfemininepl: brittleness (uncountable); fragilidades algorítmicas (countable in Portuguese)
fragilidade algorítmica
1. The property of an algorithm or machine learning model that causes it to fail or perform poorly when faced with inputs or conditions that differ from its training data or design specifications
The algorithmic brittleness of the facial recognition system became apparent when tested with different lighting conditions.
A fragilidade algorítmica do sistema de reconhecimento facial tornou-se aparente quando testado sob diferentes condições de iluminação.
2. The vulnerability of algorithms to unexpected edge cases, adversarial inputs, or minor variations that were not accounted for during development
Researchers identified algorithmic brittleness in the autonomous vehicle's decision-making when encountering unusual weather patterns.
Pesquisadores identificaram fragilidade algorítmica na tomada de decisão do veículo autônomo ao encontrar padrões climáticos incomuns.
3. The lack of robustness in algorithmic systems that makes them prone to unexpected failures or degradation in performance
The algorithmic brittleness of the chatbot was exposed when users employed sarcasm and colloquial language.
A fragilidade algorítmica do chatbot foi exposta quando usuários empregaram sarcasmo e linguagem coloquial.
This is a technical term primarily used in artificial intelligence, machine learning, and computer science communities in both Brazil and the USA. It has gained prominence with increased discussion about AI safety, reliability, and the limitations of machine learning models. The term reflects growing concerns in tech industries about deploying AI systems that may fail in real-world conditions beyond their training parameters. In Brazilian academic contexts, the Portuguese translation is preferred, though the English term is frequently used in technical papers and discussions.
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