Resources

This comprehensive survey examines the environmental impact of artificial intelligence throughout its lifecycle, from development to deployment and maintenance. The paper provides a systematic analysis of the challenges in making AI more sustainable, including hardware efficiency, algorithm design, and operational practices. The authors identify key opportunities for reducing AI’s environmental footprint and propose a research agenda for sustainable AI development.

This comprehensive study examines how algorithmic fairness principles can be effectively implemented in real-world applications. The authors analyze the gap between theoretical fairness metrics and practical challenges in deployment. The research provides concrete examples of how bias can manifest in machine learning systems and offers practical strategies for detecting and mitigating unfairness in automated decision-making systems. The paper emphasizes the importance of considering social context and stakeholder engagement in developing fair algorithms.

This comprehensive study analyzes the real carbon footprint of training large neural network models, taking into account multiple often-overlooked factors. The research provides a detailed methodology for calculating CO2 emissions and demonstrates how the choice of data center location and timing can significantly impact the environmental cost of AI training. The authors show that thoughtful choices about where and when to train models can reduce CO2 emissions by up to 100x compared to random choices.

This influential paper introduces the concept of Green AI, which encourages AI research that yields better results while consuming less computing power and thus lower environmental impact. The authors contrast Green AI with what they call Red AI: research that seeks to improve accuracy through massive computational power, regardless of the environmental cost. The paper proposes new evaluation criteria for AI research that include computational efficiency alongside accuracy, encouraging more sustainable approaches to AI development.

This foundational paper examines the ethical implications of artificial intelligence development and deployment. The authors present a comprehensive framework for ensuring AI systems are developed and used in ways that benefit humanity. The research addresses key ethical challenges including algorithmic bias, transparency, accountability, and the long-term societal impact of AI systems. The paper proposes concrete guidelines for ethical AI development and governance structures to ensure responsible innovation.

This pioneering study examines the carbon footprint of training natural language processing models. The authors quantify the financial and environmental costs of training various NLP models. The study reveals that training a single BERT model can emit as much CO2 as a trans-Atlantic flight, and that the computational costs of NLP models double every 3-4 months. The authors provide concrete recommendations to reduce environmental impact, particularly by prioritizing energy efficiency in model design and using renewable energy sources for training.

This landmark document provides comprehensive ethical principles and guidelines for computing professionals. It addresses fundamental ethical considerations in computing practice and research. The code establishes clear principles for professional responsibility, including avoiding harm, respecting privacy, ensuring fairness, and promoting sustainable computing practices. This work has become a cornerstone reference for ethical decision-making in computer science and information technology.