Improving Testing of Deep-Learning Systems

doi:10.1145/3633311

Improving Testing of Deep-Learning Systems

2024-02-22

0

OA

AI

H

Harsh Deokuliar *

R

Raghvinder S. Sangwan

DOI：10.1145/3633311

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A combination of differential and mutation testing results in better test data. ARTIFICIAL INTELLIGENCE (AI) and machine learning (ML) are finding applications in many domains. With their continued success, however, come significant challenges and uncertainties. These include: How much data is enough to train productionready AI systems, and is it even feasible? Is the data representative of the complete distribution of the problem being solved? Are the results of the system transparent and explainable? How is testing defined in the realm of AI systems? Are AI systems truly usable in safety-critical environments? What are the boundaries of the ethical use of AI systems?

Improving Testing of Deep-Learning Systems

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