Resources

This research presents a comprehensive framework for developing environmentally sustainable software, with a particular focus on AI systems and applications. The study identifies key principles and practices for green software engineering, including energy-aware design patterns, efficient coding practices, and sustainability metrics. The authors provide concrete guidelines and case studies demonstrating how to implement sustainable software development practices throughout the entire software lifecycle.

This practical guide provides concrete recommendations for implementing sustainable computing practices in AI research and development. The research outlines specific strategies for reducing energy consumption and carbon emissions throughout the AI development lifecycle, from experiment design to deployment. The authors present case studies and empirical evidence demonstrating the effectiveness of various sustainability practices in real-world AI projects.

This research introduces new methodologies for measuring and reducing the carbon footprint of AI computations across different computing environments. The study presents tools and techniques for accurate carbon impact assessment of AI workloads, considering factors such as hardware efficiency, datacenter location, and time-of-day energy mix. The authors provide practical recommendations for implementing carbon-aware computing practices in AI development and deployment.

This research examines how artificial intelligence is transforming assistive technologies, creating new opportunities and challenges for users with disabilities. The study analyzes various AI-powered assistive technologies, including advanced screen readers, intelligent voice interfaces, and computer vision systems for the visually impaired. The authors identify key success factors and potential pitfalls in developing AI-based assistive technologies, providing guidelines for creating more effective and inclusive solutions.

This paper presents a comprehensive framework for developing interpretable AI systems that can explain their decisions to stakeholders. The research bridges the gap between theoretical approaches to AI interpretability and practical implementation challenges. The authors analyze various techniques for making AI systems more transparent and understandable, including feature attribution methods, counterfactual explanations, and human-centered design approaches. The study also addresses the crucial balance between model complexity and interpretability, offering guidelines for when and how to prioritize explainability in AI systems.

The last decade saw the acceleration of new technologies adoption, shaping the digital landscape in terms of speed, quality and connectivity for multimedia contents and communication tools. While many activities have been able to benefit from the numerous innovations (4.0 industry, e-commerce, telecommunications, etc.) to develop, this growth has always been coupled with a significant increase of pressures on the environment and natural resources. The French Agency for Ecological Transition (ADEME) has proposed four scenarios for achieving carbon neutrality by 2050 in its study “Transition 2050, Choisir maintenant, Agir pour le Climat”. They aim to link the technical and economic dimensions with thoughts on the transformations in society that they imply or that they give rise to. This is the context for the study entitled “Assessment of the environmental impact of digital technology in France and prospective analysis”. The present report on the “prospective analysis for 2030 and 2050 and medium- and long-term courses of action”, is a continuation of Task 1 on the “state of play and courses of action” and Task 2 on the “environmental assessment of digital services in France”. Specifically, Task 3 aims to assess the environmental impact of the digital sector in France for the 2030 and 2050 timeframes according to a trend scenario and different scenarios for the mitigation of this impact. Like Task 2, it consists of an evaluation of the impacts using the Life Cycle Assessment (LCA) methodology. This methodology focuses on the three thirds of the digital world: user terminals, networks and data centers. The results are presented at the France-wide scale and are detailed under several levels of analysis in order to get a more acute interpretation and a better comprehension of direct environmental stakes related to digital technologies in France.

This paper presents a methodology for accurately measuring the carbon emissions of AI workloads running in cloud environments. The research provides detailed measurements across different cloud providers and regions, showing how carbon intensity can vary significantly based on location and time of day. The authors also release tools and best practices for researchers and practitioners to measure and reduce the carbon footprint of their AI applications.

This comprehensive study examines methods for developing machine learning systems that protect individual privacy while maintaining high performance. The research analyzes various privacy-preserving techniques including differential privacy, federated learning, and secure multi-party computation. The authors provide practical guidelines for implementing privacy-preserving ML systems and evaluate the trade-offs between privacy guarantees and model utility. The paper also addresses emerging challenges in privacy-preserving ML, including new attack vectors and regulatory compliance requirements.

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.