The field of AI research is moving towards a more sustainable and ethical direction. Recent developments focus on reducing energy consumption and carbon footprint of AI models, as well as integrating ethics and environmental sustainability in AI research and practice. Researchers are exploring new methods and tools to estimate and mitigate the environmental impact of AI, such as green coding, energy-efficient ML technologies, and carbon footprint evaluation tools. Notable papers include the proposal of the e-person architecture for constructing a unified and incremental development of AI ethics, and the development of the HCI GenAI CO2ST Calculator for estimating the carbon footprint of generative AI use in Human-Computer Interaction research. The AI-Driven Framework for Multi-Service Multi-Modal Devices in NextG ORAN Systems and the evaluation of Neural Circuit Policy for estimating energy consumption of base stations are also noteworthy for their innovative approaches to reducing energy consumption in AI-based network management.
Sustainability and Ethics in AI Research
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e-person Architecture and Framework for Human-AI Co-adventure Relationship
Novel Closed Loop Control Mechanism for Zero Touch Networks using BiLSTM and Q-Learning
Green MLOps to Green GenOps: An Empirical Study of Energy Consumption in Discriminative and Generative AI Operations
Demystifying CO2: lessons from nutrition labeling and step counting
The HCI GenAI CO2ST Calculator: A Tool for Calculating the Carbon Footprint of Generative AI Use in Human-Computer Interaction Research
Bridging the Gap: Integrating Ethics and Environmental Sustainability in AI Research and Practice
Carbon Footprint Evaluation of Code Generation through LLM as a Service
Information Retrieval for Climate Impact
AI-Driven Framework for Multi-Service Multi-Modal Devices in NextG ORAN Systems
Towards Green AI-Native Networks: Evaluation of Neural Circuit Policy for Estimating Energy Consumption of Base Stations