Accountable Human-AI Deliberation with LLMs: Scaling Collective Intelligence through Symbiotic Scaffolding

Wajdi Zaghouani

arxiv: 2605.26940 · v1 · pith:5Q2UOYYGnew · submitted 2026-05-26 · 💻 cs.CL

Accountable Human-AI Deliberation with LLMs: Scaling Collective Intelligence through Symbiotic Scaffolding

Wajdi Zaghouani This is my paper

Pith reviewed 2026-06-29 17:57 UTC · model grok-4.3

classification 💻 cs.CL

keywords human-AI deliberationLLM mediationcollective intelligenceclause-level provenancediversity metricscontestability workflowssymbiotic scaffoldingdemocratic deliberation

0 comments

The pith

A three-layer symbiotic human-AI framework scales deliberation while preserving pluralism through provenance tracking and human ratification.

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

The paper claims that LLMs can overcome traditional limits on group deliberation size by generating statements participants often prefer, yet pure AI use risks flattening diverse views and eroding trust. It proposes a framework with three layers that first amplifies observed diversity, then facilitates statements with traceable clause origins, and finally requires human approval to ratify outputs. A sympathetic reader would care because this structure aims to make large-scale collective intelligence practical without the legitimacy problems that arise when people cannot contest how their positions are represented. The approach includes new metrics for coverage and erasure, a pipeline for tracing AI contributions, and workflows that let users adjust trade-offs and challenge results.

Core claim

We propose a symbiotic human-AI framework organized into three layers: observation and diversity amplification, facilitation with clause-level provenance, and human primacy for ratification. Our contributions include graded coverage, diversity, and erasure metrics with salience-aware weighting; a provenance pipeline combining cross-encoder similarity with causal knockout diagnostics; preference-conditioned trade-off control; equity-aware contestability workflows; adversarial robustness tests; and an evaluation protocol with ablation designs informed by evidence of LLM-as-judge limitations. The result is a testable blueprint for deliberation technology that scales collective intelligence whil

What carries the argument

The symbiotic human-AI framework with three layers (observation and diversity amplification, facilitation with clause-level provenance, and human primacy for ratification) that supplies traceable clause origins and human final approval.

If this is right

Graded coverage and diversity metrics with salience weighting can show how completely group statements represent participant positions.
Clause-level provenance combined with causal diagnostics lets users identify and challenge specific AI-generated content.
Preference-conditioned controls allow explicit balancing between agreement and retention of differing views.
Equity-aware workflows give all participants structured ways to contest representation regardless of background.
The evaluation protocol with ablations can test whether the layers reduce known LLM mediation failures.

Where Pith is reading between the lines

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

The provenance approach could be adapted to other AI-supported group processes such as collaborative writing or policy drafting to increase transparency.
If the layers hold up, organizations might run larger consultations without needing many human facilitators.
The contestability mechanisms suggest a general pattern for keeping human oversight in scaled AI systems.
Real-world trials would need to measure whether the metrics align with participants' own sense of fair representation.

Load-bearing premise

The metrics, provenance pipeline, preference trade-offs, and contestability workflows will actually stop pluralism from collapsing and keep outputs legitimate once the system is running.

What would settle it

A real deployment in which participants cannot trace or successfully contest how their views appear in final statements, or in which measured diversity drops despite the framework being applied.

read the original abstract

Large language models (LLMs) can support democratic deliberation at scales previously constrained by turn-taking and facilitation bandwidth. Recent work shows that LLM-generated group statements are often preferred over human-mediated outputs, while theoretical analyses argue that LLMs relax the simultaneity constraints limiting collective intelligence. Yet pure LLM mediation risks collapsing pluralism, over-optimizing for agreement, and undermining legitimacy when participants cannot contest how they are represented. We propose a symbiotic human-AI framework organized into three layers: observation and diversity amplification, facilitation with clause-level provenance, and human primacy for ratification. Our contributions include graded coverage, diversity, and erasure metrics with salience-aware weighting; a provenance pipeline combining cross-encoder similarity with causal knockout diagnostics; preference-conditioned trade-off control; equity-aware contestability workflows; adversarial robustness tests; and an evaluation protocol with ablation designs informed by evidence of LLM-as-judge limitations. The result is a testable blueprint for deliberation technology that scales collective intelligence while preserving agency and legitimacy.

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

This is a detailed proposal for a three-layer human-AI deliberation framework that lists useful components but supplies no tests or data.

read the letter

The paper lays out a symbiotic framework with three layers: observation plus diversity amplification, facilitation that includes clause-level provenance, and human ratification at the end. It also specifies graded metrics for coverage, diversity, and erasure, a provenance pipeline that mixes cross-encoder similarity with knockout checks, preference-conditioned trade-offs, equity-aware contestability steps, and an evaluation plan with ablations.

What stands out is the concrete organization of the layers and the explicit list of mechanisms meant to keep pluralism intact. The authors connect this to existing LLM mediation work and flag the risks of pure LLM outputs, then map accountability features onto those risks. That integration is the main addition.

The clear limitation is the complete absence of evidence. The abstract describes these pieces as a testable blueprint, yet no participant runs, no ablation outcomes, and no measurements appear. The assumption that the provenance, metrics, and workflows will actually stop over-optimization for agreement or preserve legitimacy is stated but not checked. Without that, the central claims stay unverified.

This is aimed at researchers working on AI-supported collective decision-making or democratic deliberation tools. Someone looking for a structured way to combine human oversight with LLM scale might pull ideas from the layer design and metric definitions. It deserves peer review because the problem is real and the blueprint is specific enough to evaluate and extend, even though any review would need to require empirical validation before acceptance.

Referee Report

2 major / 1 minor

Summary. The paper proposes a three-layer symbiotic human-AI framework for scaling democratic deliberation with LLMs: (1) observation and diversity amplification, (2) facilitation with clause-level provenance, and (3) human primacy for ratification. It contributes graded coverage/diversity/erasure metrics with salience-aware weighting, a cross-encoder + causal-knockout provenance pipeline, preference-conditioned trade-off control, equity-aware contestability workflows, adversarial robustness tests, and an ablation-informed evaluation protocol, framing the work as a testable blueprint that preserves pluralism and legitimacy.

Significance. If the proposed mechanisms prove effective in deployment, the work could meaningfully advance research on LLM-supported collective intelligence by providing concrete safeguards against over-optimization for agreement. The explicit inclusion of an evaluation protocol with ablations and LLM-as-judge limitations is a constructive element that could support future empirical work.

major comments (2)

[Abstract] Abstract and contributions list: the central claim that the three-layer framework plus graded metrics, provenance pipeline, preference-conditioned trade-offs, and equity-aware contestability workflows will 'scale collective intelligence while preserving agency and legitimacy' is presented without any empirical results, ablation outcomes, simulation data, or deployment measurements. This absence is load-bearing because the manuscript's value rests on the effectiveness of these untested components.
[Abstract] Abstract: the description of the provenance pipeline (cross-encoder similarity with causal knockout diagnostics) and the contestability workflows is given at a high level with no formal definition, pseudocode, or worked example showing how clause-level provenance prevents representation collapse or enables meaningful human contestation.

minor comments (1)

[Abstract] The abstract refers to 'adversarial robustness tests' and 'an evaluation protocol with ablation designs' but does not indicate whether these are implemented in the manuscript or left as future work.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. Our manuscript presents a proposed framework and evaluation protocol as a testable blueprint rather than an empirically validated deployment. We address each major comment below.

read point-by-point responses

Referee: [Abstract] Abstract and contributions list: the central claim that the three-layer framework plus graded metrics, provenance pipeline, preference-conditioned trade-offs, and equity-aware contestability workflows will 'scale collective intelligence while preserving agency and legitimacy' is presented without any empirical results, ablation outcomes, simulation data, or deployment measurements. This absence is load-bearing because the manuscript's value rests on the effectiveness of these untested components.

Authors: We agree that the manuscript contains no empirical results, ablations, or deployment data, as it is positioned as a design proposal and blueprint for future work rather than a completed empirical study. The central claim describes the intended function of the framework (addressing risks of pluralism collapse while enabling scaled deliberation) and is supported by the concrete mechanisms and evaluation protocol we outline; it does not assert that effectiveness has already been demonstrated. We will revise the abstract and contributions list to explicitly qualify the work as a proposal without current empirical validation, while retaining the testable elements. revision: partial
Referee: [Abstract] Abstract: the description of the provenance pipeline (cross-encoder similarity with causal knockout diagnostics) and the contestability workflows is given at a high level with no formal definition, pseudocode, or worked example showing how clause-level provenance prevents representation collapse or enables meaningful human contestation.

Authors: The abstract is intentionally concise. The full manuscript provides additional technical detail on the cross-encoder + causal-knockout pipeline and equity-aware contestability workflows. We accept that a worked example or pseudocode would improve clarity and will add one in the revision to illustrate clause-level provenance and its role in enabling contestation. revision: yes

Circularity Check

0 steps flagged

No circularity: proposal paper with no derivations or fitted predictions

full rationale

The manuscript is a conceptual proposal for a three-layer symbiotic framework and associated metrics/pipelines, presented as a 'testable blueprint' without any equations, first-principles derivations, predictions of new quantities, or parameter-fitting steps. No load-bearing self-citations, uniqueness theorems, or ansatzes are invoked to justify core claims. The central contribution is a list of design elements whose effectiveness is explicitly left for future empirical testing, so no step reduces to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no concrete free parameters, axioms, or invented entities; the description remains at the level of high-level architecture and intended metrics.

pith-pipeline@v0.9.1-grok · 5697 in / 1031 out tokens · 32841 ms · 2026-06-29T17:57:56.455058+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

21 extracted references · 3 canonical work pages

[1]

how do we let everyone talk?

Introduction Deliberation is a communicative process through which groups exchange reasons, weigh arguments, and seek decisions that can be justified to those bound by them. The normative ideal transcends mere aggregation of preferences, aiming instead for mutual justification and learning under conditions of inclusion and respect ( Habermas, 1984). Yet s...

1984
[2]

Formal definitions of coverage, diversity, and erasure metrics with graded scoring and salience -aware minority weighting (Sec - tion 4.2.1)
[3]

A clause-level provenance pipeline combining cross-encoder similarity with causal diagnostics including knockout regeneration (Section 4.2.3)
[4]

Discussion of how preference-conditioned align- ment mechanisms such as PARM (Lin et al.,
[5]

enable controllable trade-offs at inference time (Section 4.2.2)
[6]

Concrete contestability workflows with equity - aware rate limits and governance protocols, il - lustrated with a worked example (Section 4.3.1)
[7]

Robustness tests including adversarial attri - bution attacks, informed by recent work on adversarially robust authorship segmentation (Sai Teja et al., 2025) (Section 4.4)

2025
[8]

An evaluation protocol with ablation designs and validated psychometric instruments, informed by LLM-as-judge limitations (Li et al., 2025) (Sec- tion 6)

2025
[9]

Background and Related Work 2.1. Deliberation and Collective Intelligence Habermas frames communicative action as coor - dination through language oriented toward mutual understanding rather than strategic manipulation (Habermas, 1984). His discourse ethics holds that valid norms are those to which all affected parties could agree as participants in ratio...

1984
[10]

In each round t, participants submit contributions xt that may include opinions, rea - sons, evidence, narratives, or critiques

Problem Formulation and Requirements We model a deliberation episode as a sequence of rounds. In each round t, participants submit contributions xt that may include opinions, rea - sons, evidence, narratives, or critiques. The sys- tem maintains a shared representation Rt compris- ing: a topic and stance map with clusters Ct = {Ct, . . . , Ct }, candidate...

2018
[11]

what did my contribution influence?

A Symbiotic Human-AI Framework We propose a three -layer framework designed to satisfy R1–R8, with each layer specified in suffi - cient technical detail to enable implementation and evaluation. The layers form an iterative loop: infor- mation flows upward from observation to synthesis to ratification, while governance constraints flow downward. 4.1. Laye...

2001
[12]

An input ingestion module accepts contributions in parallel and stores them with times- tamps, participant identifiers, and metadata

System Architecture and Language Resources The architecture separates concerns across the three layers. An input ingestion module accepts contributions in parallel and stores them with times- tamps, participant identifiers, and metadata. Layer 1 components produce theme maps and diversity dashboards through embedding, clustering, and visualization. Layer ...

2024
[13]

Evaluation Protocol We propose an evaluation protocol informed by findings on LLM judge limitations and deliberation benchmark results, designed for implementation in future empirical studies. 6.1. Study Design A three-arm experimental design compares: (a) hu- man facilitation baseline using trained modera - tors, (b) LLM mediation without provenance or v...

2024
[14]

The deliberation log schema, formal metrics, verifiable safety properties, and evaluation protocol provide the scaffolding for controlled studies

Discussion The framework is designed as infrastructure for empirical testing rather than a final system. The deliberation log schema, formal metrics, verifiable safety properties, and evaluation protocol provide the scaffolding for controlled studies. The ablation design (Section 6) is specifically intended to iso - late whether provenance and contestabil...

2024
[15]

We view this as a necessary intermediate step: existing systems either lack for- mal specification of fairness-relevant properties or conflate endorsement with legitimacy

Limitations This paper presents a framework rather than im - plementation results. We view this as a necessary intermediate step: existing systems either lack for- mal specification of fairness-relevant properties or conflate endorsement with legitimacy. Our contribu- tion is a specification precise enough to implement, critique, and empirically test. Key...
[16]

Conclusion Scalable AI-mediated deliberation is feasible only when contestability and governance are built into the technical core rather than treated as afterthoughts. We have proposed a symbiotic framework that specifies formal metrics, causal provenance, preference-conditioned control, equity- aware contestability, adversarial robustness pro - tocols, ...
[17]

Systems that medi - ate deliberation inevitably shape whose voices are amplified and how consensus is constructed

Ethical Considerations This framework addresses AI -augmented demo - cratic deliberation, where ethical design is intrinsic to the technical contribution. Systems that medi - ate deliberation inevitably shape whose voices are amplified and how consensus is constructed. Al- though contestability and provenance mechanisms are intended to render this influen...

2025
[18]

Bakker, Daniel Jarrett, et al

References Michael Henry Tessler, Michiel A. Bakker, Daniel Jarrett, et al. 2024. AI can help humans find common ground in democratic deliberation. Sci- ence, 386(6719): eadq2852. doi: 10.1126/sci - ence.adq2852. Scott E. Page. 2025. Everyone, everywhere, all at once: LLMs and the new physics of collective intelligence. Collective Intelligence , 4(3). doi...

work page doi:10.1126/sci 2024
[19]

Journal of Deliberative Democracy

Why AI technosolutionism harms democ- racy and deliberation. Journal of Deliberative Democracy. doi: 10.16997/jdd.1839. Baijiong Lin, Weisen Jiang, Yuancheng Xu, Hao Chen, and Ying-Cong Chen. 2025. PARM: Multi- objective test -time alignment via preference - aware autoregressive reward model. In Proceed- ings of the 42nd International Conference on Ma- ch...

work page doi:10.16997/jdd.1839 2025
[20]

doi: 10.18653/v1/2024.acl-long.126. L. D. M. S. Sai Teja, N. Siva Gopala Krishna, Ufaq Khan, Elizaveta Goncharova, and Va - sudeva Varma. 2025. DAMASHA: Detect- ing AI in mixed adversarial texts via seg - mentation with human-interpretable attribu- tion. arXiv preprint arXiv:2512.04838 . doi: 10.48550/arXiv.2512.04838. David Dalrymple, Joar Skalse, Yoshua...

work page doi:10.18653/v1/2024.acl-long.126 2024
[21]

In Proceedings of the 6th Work- shop on Open-Source Arabic Corpora and Pro- cessing Tools (OSACT) @ LREC-COLING 2024, pages 20–30

Munazarat 1.0: A corpus of Arabic com - petitive debates. In Proceedings of the 6th Work- shop on Open-Source Arabic Corpora and Pro- cessing Tools (OSACT) @ LREC-COLING 2024, pages 20–30. Siwar Laabar and Wajdi Zaghouani. 2024. Multi- dimensional insights: Annotated dataset of stance, sentiment, and emotion in Facebook comments on Tunisia’s July 25 measu...

2024

[1] [1]

how do we let everyone talk?

Introduction Deliberation is a communicative process through which groups exchange reasons, weigh arguments, and seek decisions that can be justified to those bound by them. The normative ideal transcends mere aggregation of preferences, aiming instead for mutual justification and learning under conditions of inclusion and respect ( Habermas, 1984). Yet s...

1984

[2] [2]

Formal definitions of coverage, diversity, and erasure metrics with graded scoring and salience -aware minority weighting (Sec - tion 4.2.1)

[3] [3]

A clause-level provenance pipeline combining cross-encoder similarity with causal diagnostics including knockout regeneration (Section 4.2.3)

[4] [4]

Discussion of how preference-conditioned align- ment mechanisms such as PARM (Lin et al.,

[5] [5]

enable controllable trade-offs at inference time (Section 4.2.2)

[6] [6]

Concrete contestability workflows with equity - aware rate limits and governance protocols, il - lustrated with a worked example (Section 4.3.1)

[7] [7]

Robustness tests including adversarial attri - bution attacks, informed by recent work on adversarially robust authorship segmentation (Sai Teja et al., 2025) (Section 4.4)

2025

[8] [8]

An evaluation protocol with ablation designs and validated psychometric instruments, informed by LLM-as-judge limitations (Li et al., 2025) (Sec- tion 6)

2025

[9] [9]

Background and Related Work 2.1. Deliberation and Collective Intelligence Habermas frames communicative action as coor - dination through language oriented toward mutual understanding rather than strategic manipulation (Habermas, 1984). His discourse ethics holds that valid norms are those to which all affected parties could agree as participants in ratio...

1984

[10] [10]

In each round t, participants submit contributions xt that may include opinions, rea - sons, evidence, narratives, or critiques

Problem Formulation and Requirements We model a deliberation episode as a sequence of rounds. In each round t, participants submit contributions xt that may include opinions, rea - sons, evidence, narratives, or critiques. The sys- tem maintains a shared representation Rt compris- ing: a topic and stance map with clusters Ct = {Ct, . . . , Ct }, candidate...

2018

[11] [11]

what did my contribution influence?

A Symbiotic Human-AI Framework We propose a three -layer framework designed to satisfy R1–R8, with each layer specified in suffi - cient technical detail to enable implementation and evaluation. The layers form an iterative loop: infor- mation flows upward from observation to synthesis to ratification, while governance constraints flow downward. 4.1. Laye...

2001

[12] [12]

An input ingestion module accepts contributions in parallel and stores them with times- tamps, participant identifiers, and metadata

System Architecture and Language Resources The architecture separates concerns across the three layers. An input ingestion module accepts contributions in parallel and stores them with times- tamps, participant identifiers, and metadata. Layer 1 components produce theme maps and diversity dashboards through embedding, clustering, and visualization. Layer ...

2024

[13] [13]

Evaluation Protocol We propose an evaluation protocol informed by findings on LLM judge limitations and deliberation benchmark results, designed for implementation in future empirical studies. 6.1. Study Design A three-arm experimental design compares: (a) hu- man facilitation baseline using trained modera - tors, (b) LLM mediation without provenance or v...

2024

[14] [14]

The deliberation log schema, formal metrics, verifiable safety properties, and evaluation protocol provide the scaffolding for controlled studies

Discussion The framework is designed as infrastructure for empirical testing rather than a final system. The deliberation log schema, formal metrics, verifiable safety properties, and evaluation protocol provide the scaffolding for controlled studies. The ablation design (Section 6) is specifically intended to iso - late whether provenance and contestabil...

2024

[15] [15]

We view this as a necessary intermediate step: existing systems either lack for- mal specification of fairness-relevant properties or conflate endorsement with legitimacy

Limitations This paper presents a framework rather than im - plementation results. We view this as a necessary intermediate step: existing systems either lack for- mal specification of fairness-relevant properties or conflate endorsement with legitimacy. Our contribu- tion is a specification precise enough to implement, critique, and empirically test. Key...

[16] [16]

Conclusion Scalable AI-mediated deliberation is feasible only when contestability and governance are built into the technical core rather than treated as afterthoughts. We have proposed a symbiotic framework that specifies formal metrics, causal provenance, preference-conditioned control, equity- aware contestability, adversarial robustness pro - tocols, ...

[17] [17]

Systems that medi - ate deliberation inevitably shape whose voices are amplified and how consensus is constructed

Ethical Considerations This framework addresses AI -augmented demo - cratic deliberation, where ethical design is intrinsic to the technical contribution. Systems that medi - ate deliberation inevitably shape whose voices are amplified and how consensus is constructed. Al- though contestability and provenance mechanisms are intended to render this influen...

2025

[18] [18]

Bakker, Daniel Jarrett, et al

References Michael Henry Tessler, Michiel A. Bakker, Daniel Jarrett, et al. 2024. AI can help humans find common ground in democratic deliberation. Sci- ence, 386(6719): eadq2852. doi: 10.1126/sci - ence.adq2852. Scott E. Page. 2025. Everyone, everywhere, all at once: LLMs and the new physics of collective intelligence. Collective Intelligence , 4(3). doi...

work page doi:10.1126/sci 2024

[19] [19]

Journal of Deliberative Democracy

Why AI technosolutionism harms democ- racy and deliberation. Journal of Deliberative Democracy. doi: 10.16997/jdd.1839. Baijiong Lin, Weisen Jiang, Yuancheng Xu, Hao Chen, and Ying-Cong Chen. 2025. PARM: Multi- objective test -time alignment via preference - aware autoregressive reward model. In Proceed- ings of the 42nd International Conference on Ma- ch...

work page doi:10.16997/jdd.1839 2025

[20] [20]

doi: 10.18653/v1/2024.acl-long.126. L. D. M. S. Sai Teja, N. Siva Gopala Krishna, Ufaq Khan, Elizaveta Goncharova, and Va - sudeva Varma. 2025. DAMASHA: Detect- ing AI in mixed adversarial texts via seg - mentation with human-interpretable attribu- tion. arXiv preprint arXiv:2512.04838 . doi: 10.48550/arXiv.2512.04838. David Dalrymple, Joar Skalse, Yoshua...

work page doi:10.18653/v1/2024.acl-long.126 2024

[21] [21]

In Proceedings of the 6th Work- shop on Open-Source Arabic Corpora and Pro- cessing Tools (OSACT) @ LREC-COLING 2024, pages 20–30

Munazarat 1.0: A corpus of Arabic com - petitive debates. In Proceedings of the 6th Work- shop on Open-Source Arabic Corpora and Pro- cessing Tools (OSACT) @ LREC-COLING 2024, pages 20–30. Siwar Laabar and Wajdi Zaghouani. 2024. Multi- dimensional insights: Annotated dataset of stance, sentiment, and emotion in Facebook comments on Tunisia’s July 25 measu...

2024