Environmental Slow AI: five principles for generative-system design

At a glance

Type

Framework

Workflow level

Workflow unspecified / not applicable

Year

2026

Disciplines

Computer scienceEarth & environmental scienceHumanities & history

Tasks

Knowledge synthesis

Entry updated

2026-10-17

Evidence & verification

Source level

Mixed sources
Grounded in a combination of peer-reviewed and non-peer-reviewed sources; see the individual sources.

Outcome verification

Not empirically evaluated here
A documented resource or intended workflow, not a demonstrated academic outcome. No empirical benefit is established by its inclusion.

Sources checked

2026-10-17 (UTC)

Primary scope: Environmental Slow AI — primary arXiv v1 abstract: abstract-only; ICML 2026 Culture x AI — official workshop contribution list: metadata-only; SSRN official record — same-paper identity metadata excerpt: metadata-only. Publication classification is separate from substantive read scope; see limitations and entry-audit.json.

Source-checked does not mean independently reproduced. See the Method page for how these labels are assigned.

Sources (3)

Research applicability

A conceptual synthesis and design-discussion reference for research on environmentally oriented human–AI systems. The knowledge-synthesis tag describes the paper's conceptual synthesis, not an automated literature-synthesis tool.

Getting started

Editorial, untested: read the abstract's five principles, obtain the detailed paper for its examples, and discuss which design defaults each principle would change. Do not treat the list as a tested carbon-reduction recipe.

Maintenance

Sources accessed 2026-10-17; service availability, maintenance and compatibility were not tested.

License

Reuse permissions for paper, data and code require separate assessment; no blanket licence or redistribution assurance.

Workflow & human oversight

Resource-level workflow level intentionally unspecified. Conceptual references and reusable process patterns do not themselves establish an autonomous workflow; researchers retain task-specific interpretation and oversight.

Workflow level is not an evidence score; higher is not better evidence.

Approach

The author draws on environmental humanities to examine generative-system values. Each proposed principle combines a design implementation level with an interpretive layer intended to prompt reflective engagement by users and developers.

Limitations

Abstract-only substantive review plus official ICML workshop-list metadata. Detailed design examples, implementation procedures and environmental measurements were not read. The author's human-agency and sustainability framing is not a verified outcome. The arXiv comment calls this a non-archival conference paper; the official conference list confirms participation but does not establish peer-review status of the specific contribution. Mixed denotes primary abstract and official workshop documentation, not confirmed peer review.

Reproducibility

AI-agent primary-source reading and provenance checks only. No software, experiment, hardware, outcome or installation reproduced. Source verification is not outcome verification.

Adoption requirements

Editorial, untested: use the principles as a discussion lens with environmental and design specialists; establish project-specific environmental measures and rights/privacy constraints before making sustainability claims. No software or installation requirements were established.

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