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arxiv: 2604.22654 · v1 · submitted 2026-04-24 · 💻 cs.HC

Recognition: unknown

What People See (and Miss) About Generative AI Risks: Perceptions of Failures, Risks, and Who Should Address Them

Megan Li , Wendy Bickersteth , Ningjing Tang , Parv Kapoor , Khinezin Win , Peter Zhong , Jason I. Hong , Lorrie Faith Cranor
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Hoda Heidari Hong Shen
Authors on Pith no claims yet

Pith reviewed 2026-05-08 10:35 UTC · model grok-4.3

classification 💻 cs.HC
keywords generative AIrisk perceptionsfailure modessurvey instrumentAI literacypublic awarenesssociotechnical risksstakeholder responsibilities
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The pith

A survey instrument grounded in real incidents effectively measures how people perceive generative AI risks and responsibilities.

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

The paper develops and tests a survey tool to gauge public awareness of generative AI failure modes, the risks they create, and views on who should fix them. The tool uses scenarios drawn from documented incidents along with a taxonomy of recurring breakdowns. Testing with 960 U.S. participants shows the approach captures awareness tied to current daily uses and can adapt as new applications appear. Readers would care because this method can shape literacy programs and rules that match how people actually interact with the technology instead of relying on broad or abstract warnings.

Core claim

The authors created a survey instrument validated by eight experts and administered to 960 U.S. participants that assesses awareness of generative AI failure modes, associated risks, and stakeholder responsibilities using scenarios based on publicly reported incidents and a taxonomy of sociotechnical breakdowns. Results indicate the instrument effectively evaluates risk awareness in people's current contexts of use while remaining extensible to future contexts. The work concludes that AI literacy and governance efforts should align with how individuals encounter and reason about generative AI in everyday life.

What carries the argument

The validated survey instrument structured around scenarios from publicly reported incidents and a taxonomy of GenAI failure modes, used to measure awareness, risk perceptions, and views on responsibilities.

If this is right

  • AI literacy tools and interventions can be designed using data collected by this instrument to match actual public perceptions.
  • Governance approaches for generative AI should consider how people reason about risks in everyday contexts rather than abstract lists.
  • The instrument offers a repeatable method to track shifts in perceptions as new generative AI uses develop.
  • Understanding public views on stakeholder responsibilities can inform policy choices about accountability for AI harms.

Where Pith is reading between the lines

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

  • The instrument could be adapted to measure awareness gaps for specific failure modes and guide targeted education efforts.
  • Similar scenario-based surveys might apply to public perceptions of other AI systems beyond generative tools.
  • If governance incorporates these measured perceptions, it may produce policies that better match user expectations and reduce mistrust.

Load-bearing premise

The taxonomy of failure modes drawn from public incidents fully covers the risks people face in daily use, and self-reported survey answers from a U.S. sample accurately reflect broader awareness and perceptions.

What would settle it

Re-running the survey with a non-U.S. sample or after major new incident types emerge and obtaining substantially different awareness levels or responsibility views would challenge the claims of effectiveness and extensibility.

Figures

Figures reproduced from arXiv: 2604.22654 by Hoda Heidari, Hong Shen, Jason I. Hong, Khinezin Win, Lorrie Faith Cranor, Megan Li, Ningjing Tang, Parv Kapoor, Peter Zhong, Wendy Bickersteth.

Figure 1
Figure 1. Figure 1: Summary of failure modes that participants surfaced in use-case-only scenarios (responses to Q16). Most participants view at source ↗
Figure 2
Figure 2. Figure 2: Summary of stakeholders participants called upon to address potential harms of GenAI, with arrows pointing to specific view at source ↗
Figure 3
Figure 3. Figure 3: Summary of user characteristics of the 897 GenAI users in our study sample. Percentage labels are computed as % of these 897 view at source ↗
Figure 4
Figure 4. Figure 4: Participants’ reported opinions on and prior experiences with GenAI. Participants are split into three groups: Non-users, view at source ↗
Figure 5
Figure 5. Figure 5: Participants’ perceived likelihoods of each of our use-case-failure scenarios, with scenarios along the y-axis sorted from most view at source ↗
read the original abstract

Despite growing concerns about the risks of Generative AI (GenAI), there is limited understanding of public perceptions of these risks and their associated failure modes -- defined as recurring patterns of sociotechnical breakdown across the GenAI lifecycle that contribute to risks of real-world harm. To address this gap, we present a survey instrument, validated with eight subject matter experts and deployed on a sample of 960 U.S.-based participants, to assess awareness and perceptions of GenAI's failure modes, their associated risks, and stakeholder responsibilities to address them. To support realism and content validity, our instrument is structured around scenarios grounded in publicly reported incidents and a taxonomy of GenAI's failure modes. Findings suggest that our instrument is (1) effective for assessing risk awareness and perceptions in a way that is grounded in people's current contexts of use, yet is extensible to new contexts that will inevitably arise; and (2) potentially useful for informing the design of AI literacy tools and interventions. We argue for AI literacy and governance approaches that align with how people encounter and reason about GenAI in everyday life.

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 paper introduces a survey instrument to assess public awareness and perceptions of Generative AI (GenAI) failure modes—defined as recurring sociotechnical breakdowns—along with associated risks and stakeholder responsibilities. The instrument is built around scenarios drawn from publicly reported incidents and a taxonomy of failure modes, validated by eight subject matter experts, and deployed to a sample of 960 U.S. participants. The authors conclude that the instrument is effective for capturing risk awareness in current contexts of use, remains extensible to future contexts, and can inform the design of AI literacy tools and interventions, advocating for literacy and governance approaches aligned with everyday user reasoning.

Significance. If the validation and deployment findings hold, the work provides a practical, scenario-grounded tool for HCI researchers to study public perceptions of GenAI risks. Strengths include the use of real-world incident-derived scenarios for ecological validity, expert validation with eight SMEs, and deployment on a sizable U.S. sample of 960 participants. This supports the development of literacy interventions that match how users actually encounter and reason about GenAI, addressing a gap in understanding perceptions beyond abstract concerns.

major comments (2)
  1. [Abstract and Results] The abstract and results sections provide no statistical results, error bars, reliability metrics (e.g., Cronbach's alpha), or details on how the 960-participant deployment demonstrates effectiveness or content validity of the instrument. This leaves the central claim that the instrument is 'effective' only partially evidenced, despite the expert validation and sample size.
  2. [§3] §3 (Taxonomy and Scenario Construction): The assumption that the taxonomy derived from publicly reported incidents comprehensively captures risks people encounter in everyday use is load-bearing for the instrument's grounding and extensibility claims, yet the manuscript offers no evidence or testing that it covers unreported or emerging failure modes beyond the initial incidents.
minor comments (2)
  1. [Discussion] The discussion of generalizability could more explicitly note limitations of the U.S.-only sample when claiming broader applicability for AI literacy tools.
  2. [Appendix] Some scenario descriptions in the instrument appendix could benefit from clearer mapping to specific taxonomy categories to aid replicability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and the recommendation for minor revision. We appreciate the opportunity to strengthen the evidence presented for the survey instrument and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract and Results] The abstract and results sections provide no statistical results, error bars, reliability metrics (e.g., Cronbach's alpha), or details on how the 960-participant deployment demonstrates effectiveness or content validity of the instrument. This leaves the central claim that the instrument is 'effective' only partially evidenced, despite the expert validation and sample size.

    Authors: We agree that the abstract and results would benefit from additional quantitative details to more fully substantiate the effectiveness claim. In the revised manuscript, we will expand the results section to include reliability metrics such as Cronbach's alpha for the relevant scales, key descriptive statistics from the 960 participants (means, standard deviations), and appropriate error bars or confidence intervals. We will also add explicit discussion of how the deployment data, alongside the eight-SME validation, supports content validity and demonstrates the instrument's utility for capturing perceptions grounded in current use contexts. revision: yes

  2. Referee: [§3] §3 (Taxonomy and Scenario Construction): The assumption that the taxonomy derived from publicly reported incidents comprehensively captures risks people encounter in everyday use is load-bearing for the instrument's grounding and extensibility claims, yet the manuscript offers no evidence or testing that it covers unreported or emerging failure modes beyond the initial incidents.

    Authors: We acknowledge that the taxonomy is constructed from publicly reported incidents and does not include direct evidence or testing for coverage of unreported or emerging failure modes. The approach prioritizes ecological validity through real-world grounding and SME validation rather than claiming exhaustiveness. We will revise §3 to explicitly state this scope and limitation, while clarifying that extensibility is supported by the modular scenario structure, which permits incorporation of new incidents. We do not provide testing for unreported modes, as this would require a separate, ongoing monitoring study beyond the current scope. revision: partial

Circularity Check

0 steps flagged

No significant circularity in empirical survey study

full rationale

This is a purely empirical HCI perception study with no mathematical derivations, equations, fitted parameters, or self-referential constructs. The taxonomy derives from publicly reported incidents, the instrument is validated by external experts, and claims rest on survey data from a U.S. sample. No load-bearing step reduces by construction to prior inputs or self-citations; the design is standard and self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The work rests on standard social-science assumptions about survey validity and a domain-specific taxonomy of failure modes drawn from public incidents; no free parameters or invented entities are introduced.

axioms (2)
  • domain assumption The taxonomy of GenAI failure modes based on publicly reported incidents is comprehensive for current contexts of use.
    The instrument is explicitly structured around this taxonomy to support content validity.
  • domain assumption Self-reported perceptions from the U.S. sample reflect genuine awareness and reasoning about risks.
    Core to interpreting survey responses as evidence of public understanding.

pith-pipeline@v0.9.0 · 5533 in / 1385 out tokens · 58622 ms · 2026-05-08T10:35:51.438426+00:00 · methodology

discussion (0)

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Reference graph

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