Algorithms as Social-Ecological-Technological Systems: an Environmental Justice Lens on Algorithmic Audits
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Abstract (arXiv)
This paper reframes algorithmic systems as intimately connected to and part of social and ecological systems, and proposes a first-of-its-kind methodology for environmental justice-oriented algorithmic audits. How do we consider environmental and climate justice dimensions of the way algorithmic systems are designed, developed, and deployed? These impacts are inherently emergent and can only be understood and addressed at the level of relations between an algorithmic system and the social (including institutional) and ecological components of the broader ecosystem it operates in. As a result, we claim that in absence of an integral ontology for algorithmic systems, we cannot do justice to the emergent nature of broader environmental impacts of algorithmic systems and their underlying computational infrastructure. We propose to define algorithmic systems as ontologically indistinct from Social-Ecological-Technological Systems (SETS), framing emergent implications as couplings between social, ecological, and technical components of the broader fabric in which algorithms are integrated and operate. We draw upon prior work on SETS analysis as well as emerging themes in the literature and practices of Environmental Justice (EJ) to conceptualize and assess algorithmic impact. We then offer three policy recommendations to help establish a SETS-based EJ approach to algorithmic audits: (1) broaden the inputs and open-up the outputs of an audit, (2) enable meaningful access to redress, and (3) guarantee a place-based and relational approach to the process of evaluating impact. We operationalize these as a qualitative framework of questions for a spectrum of stakeholders. Doing so, this article aims to inspire stronger and more frequent interactions across policymakers, researchers, practitioners, civil society, and grassroots communities.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 May 2023.
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Citing papers per year: 2023: 1; 2024: 6; 2025: 2; 2026: 3.
12 papers.
- 2024AI Alignment through Reinforcement Learning from Human Feedback? Contradictions and Limitationscited by 11
- 2023Towards Environmentally Equitable AI via Geographical Load Balancingcited by 10
- 2024Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspectivecited by 9
- 2024Addressing the regulatory gap: moving towards an EU AI audit ecosystem beyond the AI Act by including civil societycited by 7
- 2024(Un)making AI Magic: a Design Taxonomycited by 5
- 2024AI, Climate, and Regulation: From Data Centers to the AI Actcited by 4
- 2024AI and Identitycited by 2
- 2025Limits to AI Growth: The Ecological and Social Consequences of Scalingcited by 2
- 2025Do Chinese models speak Chinese languages?cited by 2
- 2026When Identity Overrides Incentives: Representational Choices as Governance Decisions in Multi-Agent LLM Systemscited by 0
- 2026The Global Landscape of Environmental AI Regulation: From the Cost of Reasoning to a Right to Green AIcited by 0
- 2026Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AIcited by 0
- 2026Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AI15 Jul 2026
- 2026The Global Landscape of Environmental AI Regulation: From the Cost of Reasoning to a Right to Green AI10 Feb 2026
- 2026When Identity Overrides Incentives: Representational Choices as Governance Decisions in Multi-Agent LLM Systems15 Jan 2026
- 2025Do Chinese models speak Chinese languages?31 Mar 2025
- 2025Limits to AI Growth: The Ecological and Social Consequences of Scaling29 Jan 2025
- 2024AI, Climate, and Regulation: From Data Centers to the AI Act9 Oct 2024
- 2024AI Alignment through Reinforcement Learning from Human Feedback? Contradictions and Limitations26 Jun 2024
- 2024(Un)making AI Magic: a Design Taxonomy22 Mar 2024
- 2024AI and Identity29 Feb 2024
- 2024Addressing the regulatory gap: moving towards an EU AI audit ecosystem beyond the AI Act by including civil society26 Feb 2024
- 2024Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective9 Jan 2024
- 2023Towards Environmentally Equitable AI via Geographical Load Balancing20 Jun 2023