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Researchers at University of Notre Dame investigated how hardware systems can contribute to the fairness of AI .
Their paper, published in Nature Electronics , identifies ways in which emerging hardware designs, such as computing-in-memory (CiM) devices, can affect fairness of DNNs.
The researchers suggest employing noise-aware training strategies to improve training AI models without significantly increasing computational demands.
Researchers plan to develop cross-layer co-design frameworks that optimize neural network architectures for fairness while considering hardware constraints.
They also plan to devise adaptive training techniques that could address the variability and limitations of different hardware systems.
These techniques could ensure that AI models remain fair irrespective of the devices they are running on and the situations in which they are deployed.
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