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

TL;DR

This paper proposes a sustainable AI framework inspired by human cognition, emphasizing incremental learning, carbon-aware optimization, and human collaboration to reduce environmental impact and improve adaptability.

Contribution

It introduces Human AI (HAI), a novel paradigm combining biological insights with dynamic architectures for eco-friendly, continuous, and context-aware AI systems.

Findings

HAI enables more energy-efficient AI training and deployment.

It reduces reliance on large static datasets and human annotation.

The framework improves adaptability and accountability in AI systems.

Abstract

The rapid advancement of Artificial Intelligence (AI) has led to unprecedented computational demands, raising significant environmental and ethical concerns. This paper critiques the prevailing reliance on large-scale, static datasets and monolithic training paradigms, advocating for a shift toward human-inspired, sustainable AI solutions. 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. Our approach addresses pressing challenges in active learning, continual adaptation, and energy-efficient model deployment, offering a pathway toward responsible, human-centered artificial intelligence.