To create human-centered AI that prioritizes environmental sustainability, developers must expand their evaluation frameworks beyond algorithmic fairness to include ecological impact. This involves shifting from purely technical metrics to holistic measurements of the total carbon footprint produced during the entire machine learning lifecycle.
First, developers should implement energy-tracking tools during the model training phase. Measuring the kilowatt-hours consumed by hardware can provide a direct baseline for carbon emissions. Second, it is essential to consider the carbon intensity of the energy grids used by data centers. Using models that require less computational overhead or employing techniques like model pruning and quantization can significantly reduce the energy required for both training and inference.
Finally, integrating these environmental costs into the optimization function of a model allows for a trade-off analysis between predictive accuracy and carbon efficiency. By quantifying the environmental cost per unit of accuracy, developers can make informed decisions that respect both human fairness and the planetary boundaries. This approach ensures that AI advancements do not come at the expense of long-term environmental health.
