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What Do People Want to Know About Artificial Intelligence (AI)? The Importance of Answering End-User Questions to Explain Autonomous Vehicle (AV) Decisions

T0 review · 5 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Passengers understand autonomous-driving decisions best when a static explanation is followed by a Q&A dialogue.

desk verdict Useful passenger question taxonomy and a solid demonstration that static explanatory text improves comprehension, but the paper's interactivity claim is not supported by its own pairwise contrasts. read the letter →

arxiv 2505.06428 v1 pith:GZ5LWFHU submitted 2025-05-09 cs.HC cs.AI

Somayeh Molaei , Lionel P. Robert , Nikola Banovic This is my paper · ORCID
classification cs.HCcs.AI
keywords ExplainableAIAutonomousvehiclesConversationalXAIUser-ledexplanationsliteracyHuman-centeredInteractivePassengerquestions
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper asks what passengers actually want to know about the AI driving their self-driving car, and whether answering those questions improves their understanding. In two user studies the authors first collected over 300 spontaneous passenger questions, which fall into five needs: tracking the vehicle's context and status, diagnosing events, contesting decisions, repairing trust after incidents, and learning what the AI can and cannot do. They then tested four explanation formats and found that a static text explanation followed by an interactive question-and-answer dialogue produced the highest comprehension of driving scenarios, significantly above both watching videos with no explanation and a question-answer interface without the static seed. The same combined format also raised AI literacy scores marginally relative to baseline. The authors argue that next-generation explainable AI for autonomous vehicles should be dialogue-based and grounded in the passenger's own questions rather than a fixed set of engineer-authored justifications.

What carries the argument

The load-bearing machinery is the two-stage conversational explainable AI setup: a static natural-language explanation, created by rewriting expert narratives from the vehicle's perspective, that seeds the dialogue, followed by a chat interface that answers follow-up questions using a large language model grounded in expert-annotated scenario metadata. The four experimental conditions isolate the contribution of each stage. The measurement instrument is a multiple-choice scenario-comprehension quiz built from the expert metadata, plus an AI-literacy quiz based on established AI literacy competencies. The question taxonomy from the initial Wizard-of-Oz study supplies the categories of needs that such a system must be able to handle.

What would settle it

Re-run the Study 2 experiment using footage and explanation content drawn from actual autonomous vehicles, such as disengagement or incident logs from operating self-driving fleets, rather than human naturalistic driving data; the central claim would be refuted if the static+Q&A condition no longer outperformed the video-only baseline on the scenario-comprehension quiz.

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Extended reading notes

Core claim

The central claim is that end-user understanding of AI-driven autonomous vehicle decisions is best served by layering live question answering on top of a static, task-focused explanation rather than by any single explanation format. On a driving-scenario comprehension quiz, the static and static+Q&A conditions both outperformed the video-only baseline, and static+Q&A also outperformed Q&A-only, which did not differ significantly from baseline; the overall effect of condition was significant, $F(3,70)=18.31$, $p<0.001$. The accompanying qualitative finding is that passenger questions cluster into five categories—AV context and status, hypothesizing and diagnosing events, contesting decisions, repairing trust, and learning system capabilities—most of which existing engineer-oriented explainable AI tools do not answer. The paper concludes that explanation systems should support a dialogue, seed the conversation to compensate for passengers not knowing what to ask, and cover the socio-technical context rather than only model internals.

Load-bearing premise

Participants were told the videos were recorded from a fully autonomous vehicle, but the footage actually shows human drivers, including human errors such as distraction and unsafe maneuvers, so the study assumes that human naturalistic driving incidents and expert narratives about them are a valid stand-in for genuinely AI-made driving decisions and their explanations.

Editorial extensions

If this is right

  • A conversational explanation interface that only answers questions, without first giving a static account of what happened, does not measurably improve scenario comprehension, so designers should treat a seed explanation as necessary context for useful follow-up questions.
  • Passengers will ask to diagnose, contest, and repair trust after critical events, so AV explanation systems should include post-incident functions such as damage assessment, fault attribution, and next-action planning, not just 'why' justifications.
  • LLM-generated answers that stay within the scenario metadata are highly accurate for scenario-related questions, while answers to general AI questions are much less reliable, so the knowledge base must be expanded before conversational explainable AI can teach broader AI literacy.
  • The five-category taxonomy gives designers a concrete checklist for evaluating whether an explanation interface supports situation awareness, diagnosis, contestation, trust repair, and learning about capabilities.
  • Because workload did not differ across conditions, richer interactive explanations can be added without imposing additional cognitive burden on passengers.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The seed-then-answer pattern suggests a general design rule for explainable AI beyond driving: give the user a narrative of what happened first, because users often do not know what to ask, and then let follow-up questions target their specific gaps.
  • The five-category taxonomy may be reusable as an auditing instrument for explanation systems in other high-stakes AI contexts where users need to diagnose, contest, and rebuild trust, such as medical or financial decision aids.
  • A testable extension is to vary the order and length of the static seed (full narrative versus a one-sentence summary) to find the minimum context needed before the question-and-answer stage becomes effective.
  • Because participants still failed many technical AI-literacy items even in the best condition, an explanation system that aims to raise literacy may need to proactively introduce technical topics rather than wait for users to ask about them.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 4 minor

Summary. The paper reports two user studies on end-user explainability for autonomous vehicles (AVs). Study 1 (N=17) uses a Wizard-of-Oz conversational interface with SHRP2 naturalistic driving videos, presented as if recorded from a fully autonomous vehicle, to elicit and taxonomize passenger questions about AI-driven AV behavior. Five question categories are identified, including situational awareness, hypothesis testing, contesting decisions, trust repair, and general AI curiosity. Study 2 (N=83) compares four between-subjects conditions—baseline, static text explanation, Q&A explanation, and static+Q&A—on driving-scenario comprehension, AI literacy, and workload. The authors report a significant overall effect of condition on scenario comprehension (F(3,70)=18.31, p<0.001), a marginal effect on AI literacy (p=0.0518), and no workload differences. They conclude that interactive text-based explanations improve end-users' understanding of AV decisions and inform the design of human-centered XAI.

Significance. If the central claim were supported, the paper would make a useful contribution to human-centered XAI for autonomous driving: it provides a taxonomy of passenger questions, a prototype conversational XAI system, and an attempt at objective comprehension assessment. The authors should also be credited for transparent reporting of effect sizes, Holm-Bonferroni-corrected ART-c contrasts, open prototype code, and an explicit discussion of LLM answer accuracy. However, the headline claim that answering end-user questions improves comprehension is not supported by the data as analyzed. The reliable result is that static narrative explanations improve quiz performance; adding Q&A to static explanations yields no measurable gain, and Q&A alone yields no significant gain over baseline. The paper's value therefore depends on substantially reframing the contribution from 'interactivity matters' to 'static natural-language explanations help, while the additional benefit of follow-up Q&A remains to be demonstrated.'

major comments (5)
  1. [Abstract and §4.5.1, Table 2] The abstract and introduction state that interactive text-based explanations improved comprehension of AV decisions, but the pairwise contrasts in Table 2 do not support this. Q&A alone is not significantly different from baseline (p=0.467), and static+Q&A is not significantly different from static alone (p=0.467, estimate -6.3 points). The significant omnibus effect F(3,70)=18.31 is carried by the presence of static text: both static and static+Q&A beat baseline and Q&A-only. The manuscript should be rewritten so that the central claim is 'static natural-language explanations improved comprehension scores,' with interactivity reported as an unconfirmed or limited result.
  2. [§4.1 and §4.5.1] Study 2's design is effectively a 2x2 factorial (static explanation present/absent, Q&A present/absent), but the analysis is reported only as a one-way ANOVA with pairwise contrasts. Because the central question is whether Q&A adds value beyond static text, the authors should also report a factorial ANOVA with the interaction term. The current analysis leaves the key interaction untested and the interpretation underdetermined, especially given the non-significant static vs. static+Q&A contrast.
  3. [§4.1 and Appendix B] The driving-scenario comprehension quiz was constructed directly from the expert-annotated metadata, and the same metadata were used to generate the static narratives and the LLM prompts. Participants in the static and static+Q&A conditions therefore received the answers to many quiz questions in advance. The paper itself concedes in §4.5.4 that the assessment may reward having the information readily available. As a result, the measured comprehension gains may reflect verbatim information availability rather than understanding of AI decision-making. The authors should either add a transfer task with novel scenarios, or explicitly reframe the measure as an 'information availability' check and temper the claims about understanding.
  4. [§4.5.2 and Table 3] The AI literacy result is marginal (p=0.0518), and none of the corrected pairwise comparisons reaches significance. This result cannot carry the paper's interactive-explanation claim. If the authors wish to claim an AI literacy benefit for static+Q&A, they need a preregistered and adequately powered test; the current study was powered for a large effect (f=0.4) on the primary outcome, and the AI literacy data should be treated as exploratory.
  5. [§3.4 and §4.3] Participants were told the videos were recorded from a fully autonomous vehicle, but the SHRP2 NDS footage shows human drivers, including human errors such as distracted driving and unsafe maneuvers. The explanations and quiz questions consequently attribute human errors to the AI. The paper should explicitly discuss whether human naturalistic driving incidents can stand in for AI-driven AV decisions; if actual AV behavior differs, the passenger question taxonomy and the measured comprehension effects may not transfer. At minimum, this is a boundary condition on all conclusions and should be flagged in the limitations.
minor comments (4)
  1. [§3.6] The reported question-category percentages do not sum to 100% (15.5 + 18.5 + 18.5 + 23.9 + 23.3 = 99.7). Please clarify whether the remaining 0.3% is rounding or a small uncategorized residue.
  2. [Figure 6] The x-axis label 'Not Releted' is misspelled; it should be 'Not Related'.
  3. [§4.1 and §4.5.1] The terms 'driving scenario comprehension' and 'task expertise' are used interchangeably. Please define the relationship between the two at first use to avoid confusion.
  4. [Appendix C, Q3] Question 3 in the AI literacy quiz is phrased as 'Select all that apply,' but the scoring method for this question is not described; please specify whether full or partial credit was awarded.

Circularity Check

1 steps flagged · score 6.0 of 10

The scenario-comprehension outcome is defined by the same expert metadata that generates the explanations, so the main comprehension effect partly reduces to answer availability.

  1. self definitional [Section 4.1 (Study 2 Method), with corroboration in Section 4.5.4]
    "To measure participants’ driving scenario comprehension, we created a multiple choice quiz (MCQ), replacing the high-level comprehension quiz from the first study. To create this quiz (Appendix B), we used responses from the expert-annotated metadata as both selector (i.e., the correct MCQ choices) and distractors (i.e., wrong MCQ choices)."

    The operational measure of the paper’s central outcome (driving scenario comprehension) is built from the same expert-annotated SHRP2 metadata that is the sole content of the explanation interventions: the static text is “created by modifying the expert narratives in the dataset,” and the LLM Q&A is prompted with “expert-annotated metadata and narrative for each driving scenario.” A participant who receives the static explanation is therefore shown, by construction, the very metadata values the quiz asks about; the measured comprehension gain is partly an availability effect rather than evidence of understanding AI decisions.

full rationale

The paper is an empirical HCI study rather than a formal derivation, so most of its contribution is not circular in the mathematical sense. Study 1’s taxonomy comes from Wizard-of-Oz question collection and affinity diagramming, and the AI-literacy questionnaire is tied to the external competency framework of Long and Magerko [56]. The circularity concern is localized to the driving-scenario-comprehension assessment: the correct answers are expert metadata values, and the same metadata is fed directly into the static explanations and LLM answers. Consequently, the significant omnibus effect (F(3,70)=18.31) and the static/static+Q&A advantage over baseline partially reflect the fact that these participants received the quiz content in advance. This is a genuine by-construction coupling between intervention content and outcome measure, acknowledged by the authors as a possible limitation of their explanation efficiency assessment method. The interactivity claim is further weakened because the pairwise static vs. static+Q&A contrast is not significant and Q&A-only does not beat baseline, but that is a statistical-interpretation problem rather than an additional circularity. On balance, the central scenario-comprehension result is partially circular, while the AI-literacy and question-taxonomy contributions have independent content; hence a moderate score of 6.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

No free parameters or invented entities apply to this empirical study. The central claim rests on the domain assumptions listed above, especially the simulation of AV behavior using human driving videos and the validity of the self-designed comprehension quiz.

assumptions (5)
  • domain assumption SHRP2 NDS naturalistic human driving videos represent what a passenger would experience in an AI-driven AV.
    Participants were told the videos came from a fully autonomous vehicle (Section 3.4), but the footage and metadata describe human drivers and human errors; generalizing from human incidents to AV behavior is assumed.
  • domain assumption The expert-annotated metadata and narratives are complete enough to answer passenger questions truthfully.
    The wizard and LLM were restricted to this metadata, and answers to questions outside it were 'I don't know'; if the metadata is incomplete, explanations may misrepresent AV reasoning.
  • domain assumption The multiple-choice driving scenario quiz measures understanding of AV decisions.
    The quiz was constructed from the same metadata used in explanations (Section 4.1, Appendix B); its validity as a comprehension measure is assumed, not established against external benchmarks.
  • domain assumption The LLM (GPT-3 Davinci-003) with role prompting provides acceptable explanation behavior.
    Reported accuracy is 95% for scenario-related questions and roughly 48-61% for general questions; the model may hallucinate, and answer quality varied by question type (Section 4.5.4).
  • domain assumption The recruited convenience samples represent future AV passengers.
    Study 1 had 17 and Study 2 had 83 participants, mostly licensed drivers ages 18 and up, biased toward older adults in Study 2; no claim of representativeness is made.

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Pith. "Pith review of What Do People Want to Know About Artificial Intelligence (AI)? The Importance of Answering End-User Questions to Explain Autonomous Vehicle (AV) Decisions." pith.science (2026). https://pith.science/paper/GZ5LWFHU

@misc{pith2026250506428,
  author       = {Pith},
  title        = {Pith review of: What Do People Want to Know About Artificial Intelligence (AI)? The Importance of Answering End-User Questions to Explain Autonomous Vehicle (AV) Decisions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GZ5LWFHU}},
  note         = {Machine review of arXiv:2505.06428}
}
read the original abstract

Improving end-users' understanding of decisions made by autonomous vehicles (AVs) driven by artificial intelligence (AI) can improve utilization and acceptance of AVs. However, current explanation mechanisms primarily help AI researchers and engineers in debugging and monitoring their AI systems, and may not address the specific questions of end-users, such as passengers, about AVs in various scenarios. In this paper, we conducted two user studies to investigate questions that potential AV passengers might pose while riding in an AV and evaluate how well answers to those questions improve their understanding of AI-driven AV decisions. Our initial formative study identified a range of questions about AI in autonomous driving that existing explanation mechanisms do not readily address. Our second study demonstrated that interactive text-based explanations effectively improved participants' comprehension of AV decisions compared to simply observing AV decisions. These findings inform the design of interactions that motivate end-users to engage with and inquire about the reasoning behind AI-driven AV decisions.

Figures

Figures reproduced from arXiv: 2505.06428 by the authors.

Figure 1
Figure 1. Wizard-of-Oz Design Probe User Interface showing: a) 30-second-long driving scenario video recording [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Prototype conversational XAI user interface showing: a) static natural language explanation for a [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗
Figure 3
Figure 3. Participant scores across the four conditions: a) task expertise score (i.e., driving scenario understand [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Percentage of participants who correctly answered each AI literacy assessment question. [PITH_FULL_IMAGE:figures/full_fig_p015_4.png]
Figure 5
Figure 5. Figure 5: The frequency of driving scenarios (y-axis) per number of questions that the participants asked in [PITH_FULL_IMAGE:figures/full_fig_p017_5.png]
Figure 6
Figure 6. Figure 6: Comparison of accuracy of LLM-generated answers to participant questions both related and not [PITH_FULL_IMAGE:figures/full_fig_p018_6.png]

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.