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arxiv: 2606.23192 · v1 · pith:RGJZ4AOCnew · submitted 2026-06-22 · 💻 cs.CY

Tackling "AI against sustainability"

Stefanie Kunkel , Malcolm Campbell-Verduyn , Simon Driscoll , Silke Niehoff , Gabrielle Samuel This is my paper

Pith reviewed 2026-06-26 06:14 UTC · model grok-4.3

classification 💻 cs.CY
keywords AIsustainabilityenvironmental impactregulationAI ethicsfossil fuelstargeted advertisingstakeholder dialogue
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0 comments X

The pith

Current debates miss how AI applications in fossil fuel extraction and advertising actively harm the environment, and a three-pronged strategy of regulation, industry commitment, and stakeholder dialogue can address this gap.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Current debates split AI-sustainability discussions into two camps: the resource demands of the systems themselves and the ways AI might aid environmental goals. The paper identifies a third category where AI use worsens environmental damage, for instance by locating more fossil fuel reserves or driving consumption via targeted ads. It distinguishes the technology as an object from the specific ways it gets deployed and argues that closing this gap requires coordinated action rather than isolated fixes. A reader would care because failing to account for these negative applications risks letting AI increase net environmental harm despite its other uses.

Core claim

The paper establishes that debates on AI and sustainability are dichotomised between the environmental impact of AI systems themselves and AI's potential for environmental benefit, overlooking 'AI against sustainability' as the negative environmental consequences from applications in sectors such as fossil fuel extraction and targeted advertising. It calls for a systemic understanding that separates AI as an object from its application and proposes addressing the issue through strengthened regulation, proactive self-commitment by industry, and constructive dialogue among stakeholders to move beyond isolated actions.

What carries the argument

The distinction between AI as an object and its application, which isolates 'AI against sustainability' as the negative environmental effects from specific uses that existing debates overlook.

If this is right

  • Regulation must expand to cover AI applications that increase environmental harm rather than only addressing the systems' own energy use.
  • Industry must adopt upfront commitments to avoid deploying AI in sectors that exacerbate emissions or resource depletion.
  • Stakeholder dialogue across disciplines becomes necessary to identify and close blind spots in how AI gets used.
  • Sustainability efforts for AI must shift from isolated technical fixes to collaborative, application-focused strategies.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Frameworks for responsible AI would need to treat environmental harm from deployment as a distinct evaluation category alongside technical efficiency.
  • Environmental impact assessments could become standard for AI projects in high-risk sectors such as energy and marketing.
  • This perspective connects to broader questions of how digital tools amplify physical resource extraction and consumption patterns.

Load-bearing premise

The premise that existing debates remain strictly divided into two camps and systematically miss the negative environmental effects of AI when applied in sectors like fossil fuel extraction and targeted advertising.

What would settle it

A comprehensive review of recent AI-sustainability literature that finds substantial coverage of negative applications in fossil fuel or advertising sectors would undermine the claim of a crucial overlooked gap.

read the original abstract

Current debates on AI and sustainability are dichotomised, tending to focus on either the environmental impact of AI systems themselves ("sustainability of AI") or AI's potential for environmental benefit ("AI for sustainability"). This perspective highlights a crucial gap: "AI against sustainability" - the negative environmental consequences stemming from the application of AI technologies. While AI can offer solutions, its use in sectors like fossil fuel extraction or targeted advertising can exacerbate environmental harms, often overlooked in existing discussions. We argue for a systemic understanding of these impacts, distinguishing between AI as an object and its application, and propose a three-pronged approach to tackle "AI against sustainability" by (a) strengthened regulation, (b) proactive self-commitment by industry, and (c) constructive dialogue among stakeholders. Addressing the blind spots of "AI against sustainability" requires moving beyond isolated actions and fostering collaboration across disciplines to ensure truly more sustainable AI.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript is a perspective paper arguing that debates on AI and sustainability are dichotomized into 'sustainability of AI' (environmental impacts of AI systems) versus 'AI for sustainability' (AI's potential benefits), overlooking 'AI against sustainability'—negative environmental effects from AI applications in sectors such as fossil fuel extraction and targeted advertising. It calls for distinguishing AI as an object from its applications and proposes a three-pronged approach of strengthened regulation, proactive industry self-commitment, and constructive stakeholder dialogue to address the gap.

Significance. If the framing and proposal hold, the paper could usefully broaden AI sustainability discourse by surfacing application-level harms that are rhetorically sidelined, thereby supporting more integrated policy thinking across disciplines. Its contribution is conceptual rather than empirical or formal.

major comments (2)
  1. [Abstract] Abstract: the premise that negative environmental consequences from AI applications are 'often overlooked in existing discussions' is load-bearing for the identification of the gap and the subsequent proposal, yet the text provides no citations or concrete examples of debates that systematically exclude these cases.
  2. [Abstract] Abstract: the three-pronged approach is presented as the solution without any discussion of mechanisms, precedents, feasibility constraints, or differentiation from existing AI governance initiatives, which undercuts the actionability of the central prescriptive claim.
minor comments (1)
  1. The distinction between 'AI as an object' and 'its application' is invoked but left at a high level of generality; a brief illustrative example would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our perspective paper. We address the two major comments point by point below and indicate where revisions will be made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the premise that negative environmental consequences from AI applications are 'often overlooked in existing discussions' is load-bearing for the identification of the gap and the subsequent proposal, yet the text provides no citations or concrete examples of debates that systematically exclude these cases.

    Authors: We agree that the claim requires explicit support to be fully convincing. In the revised manuscript we will add concrete examples and citations to debates and reports (e.g., IPCC-related AI assessments and major AI ethics surveys) that focus exclusively on the environmental footprint of AI systems or on beneficial applications while omitting application-level harms in sectors such as fossil-fuel extraction and targeted advertising. This will better substantiate the identified gap. revision: yes

  2. Referee: [Abstract] Abstract: the three-pronged approach is presented as the solution without any discussion of mechanisms, precedents, feasibility constraints, or differentiation from existing AI governance initiatives, which undercuts the actionability of the central prescriptive claim.

    Authors: As a perspective paper our proposal is deliberately high-level, yet we accept that greater elaboration would improve actionability. We will revise the body of the manuscript to include brief discussion of precedents (such as provisions in the EU AI Act), illustrative mechanisms for each prong, noted feasibility constraints, and explicit differentiation from or complementarity with existing initiatives such as the Montreal Declaration and voluntary industry codes. These additions will remain within the conceptual scope of the paper. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

This is a conceptual perspective paper with no equations, parameters, derivations, or formal claims. The argument distinguishes AI-as-object from AI-as-application and proposes a three-pronged policy approach based on observations about existing debates; none of these steps reduce by construction to internally defined quantities or self-citations. The premise that debates are dichotomised functions as framing rather than a load-bearing derivation. No patterns from the enumerated circularity kinds are present.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The paper rests on domain assumptions about the structure of existing debates and the existence of negative AI application impacts, while introducing a new conceptual category without independent evidence of its novelty or prevalence.

axioms (2)
  • domain assumption Current debates on AI and sustainability are dichotomised, tending to focus on either the environmental impact of AI systems themselves or AI's potential for environmental benefit.
    Invoked in the first sentence of the abstract as the foundation for identifying the gap.
  • domain assumption AI applications in sectors like fossil fuel extraction or targeted advertising can exacerbate environmental harms.
    Used to define the content of 'AI against sustainability' in the abstract.
invented entities (1)
  • AI against sustainability no independent evidence
    purpose: To categorize and draw attention to negative environmental consequences from the application of AI technologies that are currently overlooked.
    This is a newly introduced conceptual category presented as a crucial gap in the abstract, with no external validation or falsifiable handle provided.

pith-pipeline@v0.9.1-grok · 5689 in / 1540 out tokens · 44677 ms · 2026-06-26T06:14:10.386649+00:00 · methodology

discussion (0)

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Reference graph

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