Sustainable Computing Architectures for Ethical AI: Balancing Performance, Energy Efficiency, and Equity

Abstract

The increasing adoption of artificial intelligence (AI) presents both opportunities and challenges, particularly in addressing sustainability, energy consumption, and ethical concerns. This study introduces a novel framework for sustainable computing architectures aimed at balancing performance, energy efficiency, and equity in AI systems. The framework incorporates energy-efficient hardware, optimized algorithms, and equitable resource allocation strategies to mitigate the environmental and societal impacts of AI deployment. Quantitative analysis was conducted using a suite of AI workloads across domains such as healthcare, autonomous vehicles, and smart city management. Results indicate that the proposed framework reduces energy consumption by 38% on average compared to conventional architectures, while maintaining a computational performance level within 5% of the baseline. A dedicated equity module ensures resource access is distributed fairly across diverse user groups, reducing disparities by 26% in resource-intensive environments. Scalability tests demonstrated the framework's ability to handle workloads with up to 10 million operations per second without significant performance degradation. Furthermore, the environmental impact assessment revealed a 40% decrease in carbon footprint compared to traditional AI infrastructures. The study also integrates ethical considerations, addressing data privacy and algorithmic fairness, which improved user trust scores by 18% in a simulated deployment scenario. These findings highlight the critical importance of sustainable computing in aligning AI development with global environmental and ethical standards. The proposed framework offers a pathway for organizations to adopt AI technologies responsibly, ensuring long-term viability and social equity in AI applications.