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arxiv: 2605.15468 · v1 · pith:NRESEK7Qnew · submitted 2026-05-14 · 💻 cs.HC · cs.CY

GreenZ: A Sustainable UX Framework for Complex Digital Systems

Trisha Solanki This is my paper

Pith reviewed 2026-05-19 14:27 UTC · model grok-4.3

classification 💻 cs.HC cs.CY
keywords Sustainable UXDigital Waste TaxonomyAI SufficiencyUX FrameworkDigital SustainabilityUser Experience DesignSustainable Design Principles
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The pith

GreenZ offers a three-layer framework that classifies eight digital wastes and asks if AI is needed before building it.

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

This paper introduces GreenZ as a Sustainable UX Framework for complex digital systems. Its three layers include a Philosophy Layer of ten published principles, an Operational Frameworks Layer of five applied systems, and a Tools and Canvases Layer with audit instruments. At the core are a Digital Waste Taxonomy that names eight waste types and an AI Sufficiency Decision Model that first checks whether AI should exist in a flow. A sympathetic reader would care because the approach targets unused features, unanalyzed data, and energy-heavy AI that raise cognitive load and weaken trust in the systems people use every day.

Core claim

GreenZ is a three-layer Sustainable UX Framework whose central contributions are a Digital Waste Taxonomy classifying eight distinct waste types and an AI Sufficiency Decision Model that asks whether AI should exist in a given flow before any question of how to implement it. The framework rests on a Philosophy Layer built around ten published principles and an Operational Frameworks Layer comprising five applied systems, with a Tools and Canvases Layer supplying practical audit instruments and decision models. The paper presents the architecture and foundations while noting that GreenZ v1 remains theoretically grounded and empirically unvalidated.

What carries the argument

The AI Sufficiency Decision Model, which determines whether AI should exist in a user flow before any implementation questions, together with the Digital Waste Taxonomy that classifies eight distinct waste types.

If this is right

  • Digital systems built with the framework would accumulate fewer unused features and collect less unanalyzed data.
  • AI components would be added only when a simpler non-AI approach could not achieve the same outcome.
  • Users would encounter lower cognitive loads and retain higher trust because wasteful elements are removed at the design stage.
  • Sustainability considerations would enter UX work through concrete taxonomies and decision models rather than general guidelines alone.

Where Pith is reading between the lines

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

  • The taxonomy of eight wastes could be tested against real products to see which categories occur most often and which prove easiest to eliminate.
  • Design teams might adapt the AI Sufficiency Model as a checkpoint in existing review processes to question AI use before cost estimates begin.
  • Similar layered structures could be explored for other domains such as data privacy or accessibility where accumulation of unused elements also creates hidden costs.

Load-bearing premise

That the ten principles, five frameworks, new taxonomy, and decision model will reduce digital waste and improve sustainability in practice without empirical validation or testing.

What would settle it

A controlled study that applies GreenZ to an existing complex digital system and measures changes in unused features, unanalyzed data volume, AI energy and water costs, or user-reported cognitive load and trust against a matched system designed without the framework.

Figures

Figures reproduced from arXiv: 2605.15468 by Trisha Solanki.

Figure 1
Figure 1. Figure 1: GreenZ three-layer architecture. Each layer is inde [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
read the original abstract

Digital systems have become simultaneously more powerful and more wasteful. Features accumulate that nobody uses. Data is collected that nobody analyzes. AI is deployed at significant energy and water costs for gains that a simpler approach could have achieved. And through all of it, the people who depend on these systems quietly absorb the consequences in cognitive load, lost time, and eroded trust. This paper introduces GreenZ, a three-layer Sustainable UX Framework for complex digital systems. Its three layers are a Philosophy Layer built around ten published principles, an Operational Frameworks Layer comprising five applied systems, and a Tools and Canvases Layer of practical audit instruments and decision models. Two contributions sit at the framework's core: a Digital Waste Taxonomy classifying eight distinct waste types, and an AI Sufficiency Decision Model that asks whether AI should exist in a given flow before any question of how to implement it. GreenZ v1 is theoretically grounded but empirically unvalidated. A practitioner expert review study is underway at the time of submission. The paper presents the framework's architecture, its conceptual foundations, its position relative to existing literature, and an honest account of what remains to be established.

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

1 major / 3 minor

Summary. The paper introduces GreenZ, a three-layer Sustainable UX Framework for complex digital systems. The layers comprise a Philosophy Layer built around ten published principles, an Operational Frameworks Layer with five applied systems, and a Tools and Canvases Layer of practical audit instruments and decision models. Core contributions are a Digital Waste Taxonomy classifying eight distinct waste types and an AI Sufficiency Decision Model that asks whether AI should exist in a given flow before implementation questions. The manuscript presents the framework architecture, conceptual foundations, and positioning relative to existing literature while explicitly noting that GreenZ v1 is theoretically grounded but empirically unvalidated, with a practitioner expert review study underway.

Significance. If subsequently validated, the framework could provide a useful structured contribution to sustainable human-computer interaction by integrating established principles into a layered architecture with new taxonomies and decision tools. This approach has the potential to help practitioners systematically reduce feature bloat, unnecessary data collection, and energy costs from AI in digital systems, thereby improving both user experience and environmental outcomes in an emerging area of UX research.

major comments (1)
  1. [AI Sufficiency Decision Model description] The manuscript's central claim is the introduction and definition of the three-layer framework, Digital Waste Taxonomy, and AI Sufficiency Decision Model; these are satisfied by the act of defining and positioning them. However, the section describing the AI Sufficiency Decision Model provides no formal criteria, pseudocode, or decision tree that would allow independent application or testing, which limits the model's utility as a practical tool even as a conceptual contribution.
minor comments (3)
  1. [Digital Waste Taxonomy] A summary table listing the eight waste types from the Digital Waste Taxonomy, with brief definitions and examples, would improve clarity and scannability.
  2. [Philosophy Layer] The ten published principles in the Philosophy Layer should each be accompanied by their original citations in a dedicated subsection or table to facilitate traceability to the source literature.
  3. Consider adding a diagram illustrating the interactions among the three layers to help readers visualize the overall architecture.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their positive assessment of the paper's potential contribution to sustainable HCI and for the constructive feedback. We address the single major comment below and outline the planned revision.

read point-by-point responses

  1. Referee: The manuscript's central claim is the introduction and definition of the three-layer framework, Digital Waste Taxonomy, and AI Sufficiency Decision Model; these are satisfied by the act of defining and positioning them. However, the section describing the AI Sufficiency Decision Model provides no formal criteria, pseudocode, or decision tree that would allow independent application or testing, which limits the model's utility as a practical tool even as a conceptual contribution.

    Authors: We agree that the current description of the AI Sufficiency Decision Model remains at a high conceptual level without explicit operational elements such as formal criteria, a decision tree, or pseudocode. This presentation is consistent with the manuscript's framing of GreenZ v1 as a theoretically grounded framework rather than a fully specified implementation tool. However, we acknowledge that the absence of these details reduces the model's immediate utility for independent application or testing. In the revised version we will expand the relevant section to include a structured decision tree (with the core sufficiency questions and branching criteria) and a concise pseudocode outline of the decision process, while preserving the model's conceptual character and the paper's overall scope. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper is a conceptual proposal that defines a new three-layer framework, introduces an eight-type Digital Waste Taxonomy, and presents an AI Sufficiency Decision Model by synthesizing existing published principles and operational systems with original contributions. It explicitly states that GreenZ v1 is 'theoretically grounded but empirically unvalidated' with an expert review study underway, and makes no claims of empirical reduction, parameter fitting, or predictive validation that would require self-referential closure. No equations, fitted inputs, or load-bearing self-citations appear in the derivation chain; the central claims are satisfied by the act of defining and positioning the new elements relative to literature.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

The framework rests on the applicability of ten prior principles and five systems to digital UX without new empirical grounding or formal derivation; the taxonomy and decision model are introduced as novel but untested constructs.

axioms (2)
  • domain assumption Ten published principles form an adequate Philosophy Layer for sustainable UX
    Philosophy Layer built around ten published principles as stated in abstract.
  • domain assumption Five applied systems suffice for the Operational Frameworks Layer
    Operational Frameworks Layer comprising five applied systems.
invented entities (2)
  • Digital Waste Taxonomy no independent evidence
    purpose: Classify eight distinct waste types in digital systems
    New classification presented as core contribution.
  • AI Sufficiency Decision Model no independent evidence
    purpose: Ask whether AI should exist in a given flow before implementation questions
    New decision model introduced as central contribution.

pith-pipeline@v0.9.0 · 5722 in / 1509 out tokens · 97409 ms · 2026-05-19T14:27:32.327937+00:00 · methodology

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Reference graph

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