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This paper addresses the dual challenge of AI for sustainability and the sustainability of AI, extending the analysis to intelligent systems such as bio-inspired robotics and modular embodied-energy designs. Building on Aimee van Wynsberghe\u2019s concept of Sustainable AI, in this paper I investigate how different \u201ctechnological games\u201d conceptualize and operationalize sustainability. Bio-inspired robotics focuses on ecological embeddedness through biodegradable materials and natural design principles, whereas modular robotics pursues sustainability through energy efficiency and structural autonomy. These approaches reflect different \u201ctechnology games\u201d, treating sustainability variously as an ethical imperative, a systems-level constraint, or an efficiency goal. Although the underlying epistemologies and normative assumptions of these frameworks often conflict, in effect leading to fragmented assessments that obscure broader ethical trade-offs, I argue that these frameworks cannot philosophically evaluated in isolation. I propose therefore a pluralistic ethical framework: the Sustainable Intelligence Biosystems framework. This integrates three core dimensions: ecological values from bio-inspired robotics, practical reasoning from modular robotics, and justice-oriented ethics from Sustainable AI. The Sustainable Intelligence Biosystems Framework emphasizes the need for interdisciplinary dialogue and context-sensitive governance. The aim is to support ethically robust design, deployment, and oversight of sustainable intelligent systems.<\/jats:p>","DOI":"10.1007\/s43681-025-00830-5","type":"journal-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T11:57:22Z","timestamp":1756814242000},"page":"6091-6103","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Sustainability of AI and robotics: towards a unified framework for intelligence biosystems"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7102-7479","authenticated-orcid":false,"given":"Marco","family":"Tamborini","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,2]]},"reference":[{"key":"830_CR1","doi-asserted-by":"publisher","first-page":"100554","DOI":"10.1016\/j.envadv.2024.100554","volume":"17","author":"B Anifowose","year":"2024","unstructured":"Anifowose, B., Anifowose, F.: Artificial intelligence and machine learning in environmental impact prediction for soil pollution management-case for EIA process. 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