arxiv: 2605.02773 · v1 · submitted 2026-05-04 · 💻 cs.CY

Recognition: unknown

A Critical Pragmatism Approach for Algorithmic Fairness: Lessons from Urban Planning Theory

Jennah Gosciak , Karen Levy , Allison Koenecke

Authors on Pith no claims yet

Pith reviewed 2026-05-08 17:25 UTC · model grok-4.3

classification 💻 cs.CY

keywords algorithmic fairnesscritical pragmatismurban planningwicked problemsmachine learning ethicsstakeholder engagementalgorithm designgovernance

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The pith

Algorithmic fairness problems can be addressed by applying critical pragmatism from urban planning theory to manage value conflicts and power dynamics.

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

The paper tries to establish that algorithmic fairness issues mirror the wicked problems of urban planning, which are complex, value-laden, and involve conflicts over power and resources. A sympathetic reader would care because traditional approaches in machine learning, such as formal fairness definitions, often fail to account for the practical realities of stakeholder engagement and governance that arise in deployment. By adapting critical pragmatism from planning theory, the authors offer a flexible framework that emphasizes reflection on what practitioners actually do in the face of conflict. This is shown through specific recommendations applied to cases like mortgage lending algorithms, school choice systems, and data collection on feminicide. If the parallel holds, it could help data scientists and policymakers create algorithms that better navigate real-world ethical challenges.

Core claim

We draw a parallel between algorithmic fairness problems and urban planning, framing them as wicked problems. We argue that algorithmic fairness can learn from urban planning theory, specifically critical pragmatism, a reflective and deliberative approach to addressing such problems that considers what practitioners actually do in the face of conflict and power. We provide specific recommendations and apply them to case studies in ML and algorithm design: automated mortgage lending, school choice, and feminicide counterdata collection. Researchers and practitioners can incorporate these recommendations derived from urban planning into their ongoing work to more holistically address practical

What carries the argument

critical pragmatism, a reflective and deliberative approach to addressing wicked problems that considers what practitioners actually do in the face of conflict and power

If this is right

Existing fairness frameworks overlook practical challenges of governance, resource allocation, and stakeholder engagement that the new approach addresses.
Researchers and practitioners can incorporate specific recommendations from urban planning into their work on fair algorithm design.
The framework applies directly to automated mortgage lending, school choice systems, and feminicide counterdata collection.
It supports a more holistic response to complex ethical decisions in machine learning by accounting for power dynamics.

Where Pith is reading between the lines

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

This method could extend to other AI ethics areas like content moderation where technical rules clash with social values.
Algorithm design processes might benefit from structured deliberative consultations with affected communities, similar to urban planning meetings.
A testable extension would be to run pilot comparisons measuring whether the approach reduces deployment harms more than standard fairness audits alone.
Similar parallels may exist with wicked problems in fields like environmental policy or public health resource allocation.

Load-bearing premise

The structural similarities between urban planning conflicts and algorithmic fairness decisions are strong enough for direct transfer of critical pragmatism methods without substantial loss of applicability or need for unstated domain-specific modifications.

What would settle it

A real-world test where applying the critical pragmatism recommendations to an algorithmic fairness case produces no improvement in stakeholder conflict resolution or perceived fairness outcomes compared to using only mathematical fairness definitions would challenge the transferability of the approach.

Figures

Figures reproduced from arXiv: 2605.02773 by Allison Koenecke, Jennah Gosciak, Karen Levy.

**Figure 1.** Figure 1: Connections are depicted between urban planning theory and algorithmic fairness. Rational-comprehensive planning models and formal fairness definitions are more apt for “tame” or “benign” problems, as discussed in Rittel and Webber [133], while pragmatic planning models and holistic fairness definitions address “wicked problems” [133]. These terms are defined in Sections 2, 3, and 5.2. fairness, which has… view at source ↗

**Figure 2.** Figure 2: Shown is an abstract representation of the pragmatic influence on planning theory and ideas since Rittel and Webber [133]’s seminal work on wicked problems. The selected theories are based on discussions in [83, 81, 62]. The arrow represents the influence of these ideas over time. While algorithmic fairness has analogously taken a “participatory turn,” principles like reflective and deliberative practice a… view at source ↗

**Figure 3.** Figure 3: Presented here are the four elements of a critical pragmatism approach for algorithmic fairness: (1) reflect-in-action, (2) practice deliberation through public meetings, (3) listen critically and negotiate creatively, and (4) share practice stories. matism, we propose four recommendations for algorithmic fairness practitioners, which are shown in view at source ↗

read the original abstract

As data scientists grapple with increasingly complex ethical decisions in machine learning (ML) and data science, the field of algorithmic fairness has offered multiple solutions, from formal mathematical definitions to holistic notions of fairness drawn from various academic disciplines. However, navigating and implementing these fairness approaches in practice remains an ongoing challenge. In this paper, we draw a parallel between the types of problems arising in algorithmic fairness and urban planning. We frame algorithmic fairness problems as `wicked problems,' a term originating from the planning and policy space to describe the intractable, value-laden, and complex nature of this work. As such, we argue that the field of algorithmic fairness can learn from theoretical work in urban planning in ameliorating its own set of wicked problems. Urban planning is typically concerned with practical issues of governance, resource allocation, stakeholder engagement, and conflicts involving deep-seated differences. These are challenges that existing fairness frameworks can easily overlook. We present a flexible framework for designing fairer algorithms based on the urban planning theory approach of critical pragmatism -- a reflective and deliberative approach to addressing wicked problems that considers what practitioners actually do in the face of conflict and power. We provide specific recommendations and apply them to several case studies in ML and algorithm design: automated mortgage lending, school choice, and feminicide counterdata collection. Researchers and practitioners can incorporate these recommendations derived from urban planning into their ongoing work to more holistically address practical problems arising in fair algorithm design.

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.

Desk Editor's Note private letter to a colleague

The paper imports critical pragmatism from urban planning to reframe algorithmic fairness as a wicked problem, but the transfer stays mostly analogical without showing concrete technical changes.

read the letter

The main takeaway is that this work treats fairness issues in ML as wicked problems—value-laden, intractable, and shaped by power—and pulls in critical pragmatism from planning theory as a reflective, deliberative way to handle them. It then gives recommendations and walks them through three cases: automated mortgage lending, school choice, and feminicide counterdata collection. That parallel is the actual new piece; most fairness papers stay inside math definitions or audits, while this one looks outward to governance practices that already deal with conflicting stakeholders and no clean solutions. The recommendations land as practical on the surface, like paying attention to what practitioners actually do under conflict rather than imposing top-down metrics. The case studies help make the ideas concrete without overclaiming results. The soft spot is the transfer step itself. The paper assumes the similarities between planning conflicts and ML design decisions are strong enough to carry methods across directly, but it does not demonstrate how deliberative processes would alter specific choices like loss functions, constraint selection, or evaluation criteria. The applications read as illustrations rather than tests, so there is no evidence yet that this produces different or better outcomes than existing fairness toolkits. The argument holds together internally and engages the literature on wicked problems without circularity or invented entities. This is for fairness researchers and practitioners who already see limits in purely technical fixes and want an interdisciplinary angle on process and power. It is not for someone needing formal derivations or controlled experiments. I would send it to peer review because the thesis is coherent, the cases are relevant, and referees could usefully press on the mechanics of application and whether the analogy delivers operational value.

Referee Report

2 major / 1 minor

Summary. The paper claims that algorithmic fairness problems are 'wicked problems' similar to those in urban planning, and that critical pragmatism from urban planning theory offers a reflective, deliberative framework for designing fairer algorithms. It derives specific recommendations from this approach and applies them to case studies in automated mortgage lending, school choice, and feminicide counterdata collection, arguing that this can help address practical challenges like stakeholder conflicts and power dynamics overlooked by existing fairness frameworks.

Significance. If the analogy and transfer hold, the work could meaningfully broaden algorithmic fairness research by importing practice-oriented insights on governance and deliberation from urban planning, potentially yielding more context-sensitive and implementable fairness strategies that complement mathematical definitions. The emphasis on what practitioners actually do in the face of conflict is a strength, but the significance is tempered by the absence of demonstrated technical integration or empirical outcomes.

major comments (2)

The central transfer of critical pragmatism assumes structural equivalence between urban planning conflicts and algorithmic design decisions, but the manuscript provides no explicit mechanism showing how deliberative processes would alter concrete technical choices such as loss functions, fairness constraints, or evaluation metrics. This is load-bearing for the claim that the framework yields actionable recommendations for ML pipelines.
In the case studies (automated mortgage lending, school choice, feminicide counterdata), the applications describe high-level recommendations but do not demonstrate how stakeholder deliberation or reflective practice would produce specific modifications to the algorithms or data practices, leaving the operationalization of the framework untested and potentially ad-hoc.

minor comments (1)

The abstract states that the recommendations are 'specific' and applied to case studies, but the level of detail in the provided text remains high-level; clarifying the granularity expected in the full recommendations section would improve reader expectations.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for these constructive comments, which identify key areas where the manuscript can more clearly articulate the link between the proposed framework and technical practice. We address each point below and will revise the paper to strengthen the demonstration of operationalization.

read point-by-point responses

Referee: The central transfer of critical pragmatism assumes structural equivalence between urban planning conflicts and algorithmic design decisions, but the manuscript provides no explicit mechanism showing how deliberative processes would alter concrete technical choices such as loss functions, fairness constraints, or evaluation metrics. This is load-bearing for the claim that the framework yields actionable recommendations for ML pipelines.

Authors: We agree that the manuscript would benefit from a more explicit illustration of how deliberative outcomes can inform specific technical decisions. The critical pragmatism approach is intended to shape the upstream process of identifying relevant fairness criteria and power dynamics through stakeholder reflection, which then guides downstream choices such as which fairness constraints to impose or which metrics to prioritize. While the paper does not claim a one-to-one mapping, we will add a new subsection with worked examples showing how deliberation in one case study could lead to adjustments in model constraints or evaluation criteria, thereby making the actionability more concrete. revision: partial
Referee: In the case studies (automated mortgage lending, school choice, feminicide counterdata), the applications describe high-level recommendations but do not demonstrate how stakeholder deliberation or reflective practice would produce specific modifications to the algorithms or data practices, leaving the operationalization of the framework untested and potentially ad-hoc.

Authors: The case studies are presented as illustrative applications of the recommendations rather than as empirical tests of deliberation in action. We acknowledge that they currently remain at a high level and do not simulate the step-by-step effects of reflective practice on algorithm or data modifications. In revision we will expand each case study with additional detail on plausible deliberation outcomes and the resulting concrete changes to data practices or model specifications, while clarifying that these remain hypothetical illustrations given the theoretical nature of the contribution. revision: yes

Circularity Check

0 steps flagged

No circularity: framework imported from external urban planning theory

full rationale

The paper's derivation consists of framing algorithmic fairness issues as 'wicked problems' (a concept originating in planning literature) and proposing to adapt critical pragmatism methods from urban planning. This is a direct conceptual transfer from an external discipline rather than a derivation from the authors' prior fairness results, fitted parameters, or self-referential definitions. No equations, statistical predictions, or load-bearing self-citations appear in the provided text; the case-study applications are illustrative rather than reductions of the core claim to its inputs. The transferability assumption is an empirical or analogical claim open to external evaluation, not a circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The paper rests on two domain assumptions about problem similarity and transferability; no free parameters or invented entities are introduced.

axioms (2)

domain assumption Algorithmic fairness problems qualify as wicked problems in the sense used in urban planning and policy.
This is the opening framing that justifies importing planning theory.
ad hoc to paper Critical pragmatism developed for urban planning can be adapted into actionable recommendations for algorithm design.
This is the load-bearing transfer step that produces the proposed framework.

pith-pipeline@v0.9.0 · 5562 in / 1268 out tokens · 29456 ms · 2026-05-08T17:25:13.060288+00:00 · methodology

discussion (0)

Reference graph

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