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arxiv: 2606.10158 · v1 · pith:YI3WBSJNnew · submitted 2026-06-08 · 💻 cs.CY · cs.HC

"Where is this coming from?" Uncovering Trustworthiness Ideals in AI-powered Peripartum Information Seeking

Vaibhav Balloli , Julia Erickson , Xinyi Li , Erin MacMurray van Liemt , Alex Peahl , Elizabeth Bondi-Kelly This is my paper

Pith reviewed 2026-06-27 14:28 UTC · model grok-4.3

classification 💻 cs.CY cs.HC
keywords AI trustworthinessperipartum informationfocus groupsgovernance requirementsmaternal healthstakeholder perspectivesinspectable AImisinformation
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The pith

In maternal health, AI trustworthiness must be inspectable and not asserted by the system.

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

The paper examines how birthing people, clinicians, and health workers evaluate AI tools that answer questions about pregnancy and postpartum care. Through focus groups, it finds that these groups see trustworthiness as something that must be verifiable through visible processes rather than taken on the developer's word. Stakeholders differ on what counts as credible information but agree that systems need built-in transparency, ways to correct errors, and designs that work alongside existing support networks instead of replacing them. This matters because AI is being positioned to address information gaps in a domain with high preventable mortality and racial disparities, yet current systems leave governance under-specified. The authors surface four themes and translate them into proposed design artifacts that prioritize pluralistic verification and avoid shifting extra work onto users.

Core claim

In high-stakes health contexts shaped by historical inequities, trustworthiness must be inspectable and not asserted. While stakeholders diverge on what makes information credible, they converge on the need for transparency, recourse, and ecosystem complementarity. The analysis of the focus group discussions yields four themes and corresponding governance requirements: support for social and identity-based sensemaking, pluralistic verification practices, inspectable governance with recourse mechanisms, and ecosystem-aware integration that avoids shifting burden.

What carries the argument

Inductive thematic analysis of focus-group transcripts that extracts four governance requirements from stakeholder reactions to an AI factual-answering design probe.

If this is right

  • AI systems for peripartum information should incorporate mechanisms that let users inspect how answers are generated and challenge them.
  • Designs must accommodate pluralistic verification rather than enforcing a single source of authority.
  • Integration of AI tools should complement rather than displace existing clinical and community support structures.
  • Human-AI evaluation protocols should expand to include assessments of recourse and ecosystem fit.

Where Pith is reading between the lines

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

  • The same inspectability requirements may surface in other high-stakes domains where historical distrust of institutions exists, such as mental health or chronic disease management.
  • Developers could operationalize recourse through auditable logs or appeal channels that users can actually access without technical expertise.
  • Testing whether systems built around these four requirements measurably reduce information disparities would provide a concrete next step.

Load-bearing premise

The patterns observed in four focus groups with 24 participants from three stakeholder groups are sufficient to identify general governance requirements that hold beyond this sample.

What would settle it

A larger study with additional stakeholder groups or different geographic settings that finds no convergence on the need for inspectable governance, transparency, and recourse would undermine the central claim.

Figures

Figures reproduced from arXiv: 2606.10158 by Alex Peahl, Elizabeth Bondi-Kelly, Erin MacMurray van Liemt, Julia Erickson, Vaibhav Balloli, Xinyi Li.

Figure 1
Figure 1. Figure 1: Overview of our study with three stages. Stage 1: relevant stakeholder recruitment, Stage 2: semi-structured, synchro [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of the probe depicting an AI-powered FAQ application that matches a user query to clinician-verified [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Design probe for showing how an AI-powered FAQ app would work. [PITH_FULL_IMAGE:figures/full_fig_p019_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Design probe for showing how an AI-powered FAQ app would abstain when there are no supporting documents [PITH_FULL_IMAGE:figures/full_fig_p019_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Recruitment flyers for clinicians and birthing people. [PITH_FULL_IMAGE:figures/full_fig_p020_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Venn diagram of different sources described by stakeholders on what birthing people use for information seeking. [PITH_FULL_IMAGE:figures/full_fig_p028_6.png] view at source ↗
read the original abstract

AI-powered tools increasingly promise to fill information gaps in health, especially in domains like maternal and reproductive health that demand timely, accurate, and actionable information. This is extremely important, as the United States leads peer nations in preventable deaths, with stark racial disparities. However, current AI and NLP-powered systems aim to improve access to vetted maternal health information by routing user queries to a factual response while under-specifying the socio-technical governance structures that shape trust, use, and harm in practice. We report findings from four synchronous focus groups ($n=24$) with three stakeholder groups central to peripartum information support: birthing people, clinicians, and health workers (e.g., doulas, social workers, community health workers) exploring topics around information seeking, experience with current clinical infrastructure, misinformation, and an AI-enabled factual answering tool design probe. Our inductive analysis surfaces a central finding: in high-stakes health contexts shaped by historical inequities, trustworthiness must be inspectable and not asserted. While stakeholders diverge on what makes information credible, they converge on the need for transparency, recourse, and ecosystem complementarity. Based on the discussions, we identify four themes and governance requirements: (1) support for social and identity-based sensemaking, (2) pluralistic verification practices, (3) inspectable governance with recourse mechanisms, and (4) ecosystem-aware integration that avoids shifting burden. Building on these findings, we propose design artifacts that are mistrust-aware and promote principled governance mechanisms for transparent, pluralistic AI systems. Finally, we discuss the implications of our findings for expanding human-AI evaluations and improving the transparency of deployed AI systems.

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 / 2 minor

Summary. The manuscript reports results from four synchronous focus groups (n=24) with birthing people, clinicians, and health workers (doulas, social workers, community health workers). Using inductive thematic analysis of discussions on information seeking, clinical infrastructure, misinformation, and an AI factual-answering design probe, the authors surface the claim that trustworthiness in high-stakes peripartum AI systems must be inspectable rather than asserted. Stakeholders diverge on credibility criteria yet converge on transparency, recourse, and ecosystem complementarity; the analysis yields four themes and associated governance requirements, which the authors translate into mistrust-aware design artifacts and implications for human-AI evaluation.

Significance. If the themes are robust, the work supplies concrete, stakeholder-derived requirements for inspectable governance in AI health tools operating in contexts marked by historical inequities. It contributes to HCI and responsible-AI scholarship by linking transparency and recourse mechanisms to reduced burden-shifting and by proposing design artifacts that treat mistrust as a design input rather than a deficit to be corrected.

major comments (2)
  1. [Methods section (thematic analysis description)] Methods section (thematic analysis description): the inductive coding process, codebook development, inter-rater reliability statistics, member checking, and handling of divergent views are not reported. Because the central claim of convergence on transparency/recourse/ecosystem complementarity and the four governance requirements are derived directly from this analysis of n=24 transcripts, the absence of these details prevents verification that the themes are stable outputs rather than artifacts of the specific sample or analyst.
  2. [Findings and Implications sections] Findings and Implications sections: the manuscript moves from local observations in a convenience sample of three stakeholder groups to prescriptive “governance requirements” and design recommendations without reporting participant demographics (race, socioeconomic status, geography), recruitment strategy, or theoretical saturation. This step is load-bearing for the claim that the reported convergence applies beyond the sampled individuals and settings to broader AI governance in maternal health.
minor comments (2)
  1. [Abstract] Abstract: the phrase “four synchronous focus groups (n=24)” should clarify whether groups were homogeneous or mixed by stakeholder type, as this affects interpretation of divergence and convergence claims.
  2. [Methods] The design-probe description could include the exact prompt template or example outputs shown to participants to allow readers to assess how the probe may have shaped the elicited themes.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive report. The two major comments identify important gaps in reporting that affect the verifiability and scope of our claims. We respond to each below and indicate planned revisions.

read point-by-point responses

  1. Referee: Methods section (thematic analysis description): the inductive coding process, codebook development, inter-rater reliability statistics, member checking, and handling of divergent views are not reported. Because the central claim of convergence on transparency/recourse/ecosystem complementarity and the four governance requirements are derived directly from this analysis of n=24 transcripts, the absence of these details prevents verification that the themes are stable outputs rather than artifacts of the specific sample or analyst.

    Authors: We agree that greater transparency in the analytic process is needed. The revised manuscript will expand the Methods section to detail the iterative, team-based codebook development (two researchers independently coded a subset of transcripts, met to reconcile differences through discussion, and refined codes until consensus), how divergent views were resolved via team deliberation rather than statistical agreement, and the limited member-checking steps performed with two participants. Formal inter-rater reliability statistics were not calculated because the analysis was inductive and consensus-driven; we will state this rationale explicitly. revision: yes

  2. Referee: Findings and Implications sections: the manuscript moves from local observations in a convenience sample of three stakeholder groups to prescriptive “governance requirements” and design recommendations without reporting participant demographics (race, socioeconomic status, geography), recruitment strategy, or theoretical saturation. This step is load-bearing for the claim that the reported convergence applies beyond the sampled individuals and settings to broader AI governance in maternal health.

    Authors: We accept that these elements are necessary for readers to assess transferability. The revised manuscript will add a detailed description of the convenience sampling and recruitment strategy (via community organizations and clinical networks) and a statement on theoretical saturation (analysis continued until no new themes emerged across groups). However, granular demographic variables such as race, socioeconomic status, and geography were not collected, as the study design prioritized stakeholder role over individual identity characteristics; we will note this as a limitation and temper claims about broader applicability accordingly. revision: partial

Circularity Check

0 steps flagged

No circularity: inductive qualitative findings from focus groups

full rationale

The paper reports results of inductive thematic analysis on transcripts from four focus groups (n=24). No equations, fitted parameters, predictions, or derivations appear. The central claims (trustworthiness must be inspectable; convergence on transparency/recourse/ecosystem complementarity) are presented as outputs of the analysis rather than quantities defined in terms of the input data or reduced by self-citation. No load-bearing self-citation, ansatz smuggling, or renaming of known results is present in the provided text. The derivation chain is self-contained as standard qualitative reporting.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper is qualitative empirical work; it contains no free parameters, no invented technical entities, and relies on the standard domain assumption that inductive analysis of a modest number of focus groups can surface transferable design requirements.

axioms (1)
  • domain assumption Inductive thematic analysis of focus-group transcripts produces reliable, generalizable governance requirements for AI systems.
    Invoked when the authors move from the n=24 discussions to the four themes and design artifacts without additional validation steps stated in the abstract.

pith-pipeline@v0.9.1-grok · 5858 in / 1381 out tokens · 20419 ms · 2026-06-27T14:28:41.189167+00:00 · methodology

discussion (0)

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