Toward Carbon-Neutral Human AI: Rethinking Data, Computation, and Learning Paradigms for Sustainable Intelligence

Abstract--The rapid advancement of Artificial Intelligence (AI) has led to unprecedented computational demands, raising significant environmental and ethical concerns. We introduce a novel framework, Human AI (HAI), which emphasizes incremental learning, carbon-aware optimization, and human-in-the-loop collaboration to enhance adaptability, efficiency, and accountability. By drawing parallels with biological cognition and leveraging dynamic architectures, HAI seeks to balance performance with ecological responsibility. We detail the theoretical foundations, system design, and operational principles that enable AI to learn continuously and contextually while minimizing carbon footprints and human annotation costs. I. Introduction Artificial Intelligence (AI) has undergone unprecedented growth in the past decade, with state-of-the-art models achieving remarkable breakthroughs across domains such as natural language processing, computer vision, drug discovery, and climate modeling. However, this rapid progress comes at a substantial environmental cost. While the current AI paradigm largely emphasizes scale, i.e., more data, bigger models, and higher compute budgets, emerging research suggests that more sustainable solutions/paths are not only possible but necessary. In particular, the reliance on large, indiscriminately collected datasets is increasingly being challenged. Moreover, the COVID-19 pandemic, for example, underscored the need for agile learning systems capable of adapting rapidly to limited, evolving data.