Call for Papers

SustainableAI2027 solicits papers on all aspects of Sustainable Artificial Intelligence for sustainable and better world.
As AI systems grow in scale and societal influence, their ecological footprint and social implications demand rigorous, interdisciplinary scrutiny. Sustainability is not a constraint on AI development. It is a design principle.
The topics of the conference include, but are not limited to the following:

Tracks

Green AI & Energy Efficiency

  • Carbon footprint of model training, lifecycle assessment, efficient architectures, and hardware sustainability.
  • Energy-efficient model architectures (e.g., sparse models, neural architecture search)
  • Carbon footprint measurement and reduction strategies for AI systems
  • Hardware and software co-design for sustainable computing
  • Benchmarks and metrics for computational efficiency
  • Lifecycle analysis of AI infrastructure

Technical Foundations of Sustainable AI

  • Edge AI & Sustainable Deployment — on-device inference, IoT, reducing cloud dependency
  • AI Safety & Robustness — adversarial attacks, failure modes, red-teaming, uncertainty quantification
  • Explainable & Interpretable AI (XAI) — model transparency, human-understandable explanations, causal AI

Environment, Climate, Nature & AI Intersections

  • AI for Environmental monitoring
  • AI-driven climate modelling, forecasting, and adaptation strategies
  • AI for Sustainable Development Goals.
  • Machine learning for biodiversity conservation and ecosystem monitoring
  • Smart grids, renewable energy optimisation, and demand management
  • AI applications in sustainable agriculture, food security, and water management
  • Remote sensing and geospatial AI for environmental monitoring

Ethical & Responsible AI

  • Fairness, accountability, and transparency in AI systems
  • Bias detection, mitigation, and evaluation frameworks
  • Value alignment and human-centred AI design
  • Responsible data collection, consent, and privacy-preserving AI
  • Safety, robustness, and trustworthiness of AI models

Algorithmic Fairness & Equity in AI

  • Bias detection and mitigation, equitable outcomes, and AI's impact on marginalized communities.
  • Inclusive AI design for underrepresented and marginalised communities
  • Bridging the global AI divide: access, infrastructure, and capacity-building
  • Gender, race, and intersectional perspectives in AI development
  • AI for social good, poverty alleviation, and sustainable development goals (SDGs)
  • Participatory AI Design
  • Participatory and community-centred approaches to AI research
  • Data sovereignty
  • Inclusive approaches to AI development

Social & Interdisciplinary

  • Human-AI Collaboration — augmenting human decision-making sustainably, workforce transitions, co-design
  • AI Literacy & Education — democratising AI knowledge, capacity-building in the Global South
  • Indigenous & Local Knowledge in AI — integrating traditional ecological knowledge, decolonising AI datasets
  • AI for Sustainable Cities & Urban Planning — smart mobility, energy management, urban heat, waste reduction
  • AI in Sustainable Healthcare — resource-efficient diagnostics, global health equity, low-resource clinical tools
  • AI for Circular Economy & Industry — supply chain optimisation, predictive maintenance, waste minimisation

Sustainable Data Practices

  • Responsible data collection, storage efficiency, privacy-preserving methods, and data minimization.
  • ESG (Environmental, Social, and Governance) requirements for Data Centers
  • Data minimisation, frugal learning, and low-resource AI
  • Open datasets and data commons for sustainable AI research
  • Federated learning and decentralised approaches to data stewardship
  • Data provenance, quality, and long-term curation
  • Ethical sourcing and annotation of training data

AI Governance & Policy

  • Regulatory frameworks, corporate accountability, ESG metrics for AI, and international standards.
  • Regulatory frameworks and international standards for AI
  • Risk assessment, auditing, and certification of AI systems
  • Public-private partnerships and multi-stakeholder governance
  • National and regional AI strategies with sustainability objectives
  • Legal and compliance dimensions of AI deployment

Emerging Themes

  • Generative AI & Sustainability — environmental cost of large language models, responsible use of GenAI
  • AI & Biodiversity / Nature Positive — species monitoring, habitat modelling, nature-based solutions
  • Measuring AI's Net Impact — holistic cost-benefit frameworks beyond accuracy metrics

Call To Action

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Call To Action