Tri-dimensional sustainability of Artificial Neural Networks

This report investigates the social, environmental, and economic sustainability of Artificial Neural Networks (ANNs). The analysis avoids technical intricacies, focusing instead on broader sustainability implications. Socially, ANNs offer improved decision-making and safety evaluation but are hindered by issues such as bias, unrepresentative training data, and privacy concerns. These biases can perpetuate existing inequalities and are difficult to eliminate due to limitations in data and transparency. Environmentally, ANNs contribute to significant energy and water consumption, with mitigation strategies for carbon and water footprints often being mutually exclusive. Hardware requirements, raw material extraction, and increased resource demand due to efficiency gains further complicate their sustainability. Economically, ANNs enhance cost estimation, bankruptcy prediction, and financial fraud detection. However, their effectiveness is context-dependent and limited by information asymmetry, industry specificity, and accessibility. The report concludes that while ANNs hold promise for advancing sustainability, their widespread adoption is constrained by challenges in bias, resource consumption, and equitable access, necessitating careful consideration in future development and deployment.