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arxiv: 2605.12613 · v1 · submitted 2026-05-12 · 💻 cs.HC · cs.IR

Recognition: unknown

Creating Group Rules with AI: Human-AI Collaboration in WhatsApp Moderation

Gauri Nayak , Farhana Shahid , Aditya Vashistha , Kiran Garimella
Authors on Pith no claims yet

Pith reviewed 2026-05-14 20:15 UTC · model grok-4.3

classification 💻 cs.HC cs.IR
keywords WhatsAppgroup moderationhuman-AI collaborationspeculative designAI governanceprivacy concernssocial media rules
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0 comments X

The pith

WhatsApp admins value AI help drafting group rules yet resist full delegation over trust and privacy concerns.

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

The paper examines how WhatsApp group administrators collaborate with AI to create and manage rules in the absence of platform support. In a two-phase speculative design study with twenty admins in India, participants used Meta AI to generate rules and responded to probes about AI moderation features. They welcomed the AI for identifying overlooked rules and easing their workload. At the same time they expressed strong reservations tied to relational trust, data privacy, appropriate tone, and group-specific social context. The study shows that willingness to delegate authority depends on group type and individual admin style, and it highlights limits in current chatbot interfaces for this kind of collaborative work.

Core claim

Admins appreciated the AI's ability to surface overlooked rules and reduce their moderation burden, but they were highly sensitive to issues of relational trust, data privacy, tone, and social context, with willingness to delegate shaped by group type and admin style.

What carries the argument

A two-phase speculative design study in which admins interacted with Meta AI to co-create rules and evaluated a series of design probes illustrating AI-assisted moderation features.

Load-bearing premise

That responses to speculative design probes and interactions with Meta AI accurately predict real-world willingness to delegate moderation authority in actual WhatsApp groups.

What would settle it

A field deployment that tracks how often admins actually accept, edit, or reject AI-suggested rules inside live WhatsApp groups over multiple weeks.

Figures

Figures reproduced from arXiv: 2605.12613 by Aditya Vashistha, Farhana Shahid, Gauri Nayak, Kiran Garimella.

Figure 1
Figure 1. Figure 1: Design probes illustrating different AI-assisted scenarios for group rule creation, enforcement, and [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
read the original abstract

WhatsApp is one of the most widely used messaging platforms globally, with billions of users sharing information in private groups. Yet, it offers little infrastructure to support moderation and group governance. In the absence of platform-level oversight, group admins bear the responsibility of governing group behavior. In this paper, we explore how WhatsApp group admins collaborate with AI tools to create, enforce, and maintain group rules. Drawing on a two-phase speculative design study with 20 admins in India, we examine how participants interacted with an AI assistant (Meta AI) to co-create rules and responded to a series of probes illustrating AI-assisted moderation features. Our findings show that while admins appreciated the AI's ability to surface overlooked rules and reduce their moderation burden, they were highly sensitive to issues of relational trust, data privacy, tone, and social context. We identify how group type and admin style shaped their willingness to delegate authority, and surface the limitations of current chatbot interfaces in supporting collaborative rule-making. We conclude with design implications for building moderation tools that center human judgment, relational nuance, contextual adaptability, and collective governance.

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 / 2 minor

Summary. The paper reports findings from a two-phase speculative design study with 20 WhatsApp group admins in India. Participants first interacted with Meta AI to co-create group rules and then responded to design probes illustrating AI-assisted moderation features. Key results indicate that admins value AI for surfacing overlooked rules and reducing moderation burden, but remain sensitive to relational trust, data privacy, tone, and social context; willingness to delegate varies by group type and admin style, and current chatbot interfaces show limitations for collaborative rule-making. The work concludes with design implications for moderation tools that prioritize human judgment, relational nuance, and collective governance.

Significance. If the reported participant reactions hold, the study offers timely empirical grounding for human-AI collaboration in private-group moderation, an area with sparse prior HCI work. It surfaces concrete sensitivities around delegation that could inform more context-sensitive tools on platforms like WhatsApp, while underscoring the value of speculative methods for early-stage exploration of governance features.

major comments (1)
  1. [§5] The design implications about willingness to delegate authority (abstract and §5) rest entirely on responses to speculative probes and Meta AI interactions; no data on actual rule adoption, override rates, or emergent conflicts in live WhatsApp groups is reported, leaving the mapping from hypothetical reactions to real-world behavior untested and load-bearing for the central claim about delegation.
minor comments (2)
  1. [§3] The methods section would benefit from an explicit statement of how the 20 participants were recruited and screened for diversity across group types, to allow readers to assess the range of admin styles represented.
  2. [§4] Figure captions and probe descriptions could be expanded with one additional sentence each to clarify the exact AI output shown to participants, improving reproducibility of the speculative probes.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive review and recommendation for minor revision. We address the single major comment below by clarifying the scope of our speculative design study and strengthening the framing of our claims.

read point-by-point responses

  1. Referee: [§5] The design implications about willingness to delegate authority (abstract and §5) rest entirely on responses to speculative probes and Meta AI interactions; no data on actual rule adoption, override rates, or emergent conflicts in live WhatsApp groups is reported, leaving the mapping from hypothetical reactions to real-world behavior untested and load-bearing for the central claim about delegation.

    Authors: We appreciate the referee's point and agree that the study provides no direct observational data from live WhatsApp groups. Our work is explicitly framed as a two-phase speculative design study (Sections 3 and 4) whose purpose is to surface early user perceptions, sensitivities, and design considerations rather than to measure real-world adoption or conflict rates. The willingness-to-delegate findings are reported as participant attitudes elicited through Meta AI interactions and design probes; the design implications in Section 5 are presented as hypotheses for future tool development, not as validated behavioral predictions. We will make a partial revision: (1) add an explicit limitations paragraph in Section 5 and the abstract that qualifies the claims as speculative and calls for longitudinal field studies; (2) rephrase the abstract and Section 5 to avoid any implication of direct real-world mapping. No new empirical data will be collected, as that would require a fundamentally different study design. revision: partial

Circularity Check

0 steps flagged

No significant circularity in qualitative empirical findings

full rationale

The paper reports findings from a two-phase speculative design study involving direct participant responses to AI interactions and design probes. No mathematical derivations, equations, fitted parameters, or predictions are present. Central claims about admin appreciation for AI rule-surfacing and sensitivities to trust/privacy derive straightforwardly from the collected interview and probe data rather than reducing to self-citations, ansatzes, or internal redefinitions. The derivation chain is self-contained empirical reporting with no load-bearing self-referential steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on qualitative data from a small sample of participants in one country, with assumptions about the representativeness of the sample and the validity of speculative design probes.

pith-pipeline@v0.9.0 · 5503 in / 1037 out tokens · 23891 ms · 2026-05-14T20:15:27.090094+00:00 · methodology

discussion (0)

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