A team of researchers from Bucknell Universitys Freeman College of Management have discovered that compressing or pruning machine learning models known as neural networks can lead to improved accuracy and reduced bias in artificial intelligence (AI) applications.

Led by Professor Thiago Serra, the research team found that moderate compression of neural networks makes them more accurate and less biased. Neural networks are a type of AI method that mimics the human brains data processing capabilities.

Supported by a $174,000 grant from the National Science Foundation, the researchers developed exact neural network compression algorithms to reduce their size and improve their accessibility on regular devices. Their work has been accepted for presentation at two notable AI conferences and is expected to influence future AI development.

By removing smaller and less important connections within the neural network, the research team observed improved performance and reduced bias. The compression technique helps the neural network make correct predictions more uniformly for different groups. The teams findings were presented at NeurIPS, a leading conference on neural networks, in December.

In a separate paper, the researchers focused on determining the most efficient connections to remove from the neural network to enhance its efficiency. They extended their prior work to develop mathematical results that guide pruning decisions. This paper was presented at an international conference in France in June.

The research is an outcome of Professor Serras work dating back to 2018 and his labs two years of testing neural networks. The findings have practical implications for leveraging the capabilities of neural networks and improving their future performance.

Overall, the research suggests that neural network compression can play a crucial role in enhancing the accuracy and minimizing bias in AI applications.

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Artificial Intelligence Accuracy and Bias Can be Improved through ... - Fagen wasanni