mlsanityData to 14 Oct 2026

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

arXiv 2510.23524 ↗First posted 27 Oct 2025cs.AI cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 22 of its 39 references that are in the map. First posted on arXiv 27 Oct 2025.

Builds on

22 of 39 references.

References · 22

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10 more
  1. 2019Quantifying the Carbon Emissions of Machine Learningcited by 521
  2. 2018Meta-Learning with Latent Embedding Optimizationcited by 476
  3. 2018How to train your MAMLcited by 309
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  10. 2024Non-Uniform Illumination Attack for Fooling Convolutional Neural Networkscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Explainable Novel Category Discovery in Semantic Concept Spacecited by 1
  2. 2026AI-CARE: Carbon-Aware Reporting Evaluation Metric for AI Modelscited by 0
  3. 2026Mechanistic Interpretability of LLM Jailbreaks via Internal Attribution Graphscited by 0