{"id":"636d0c3a-6f0e-4840-8d53-db652a42cea9","arxiv_id":"2502.04526","paper_version":1,"verdict":"UNVERDICTED","confidence":"UNKNOWN","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Stakeholders governing synthetic media emphasize misrepresentation as the core problem and view technical transparency labels as necessary but insufficient.","lead":"This paper interviews 23 people who make or influence rules about AI-generated images, videos, and audio, and analyzes two real-world governance efforts. It finds that stakeholders see misrepresentation, not AI itself, as the core problem, and that trust and timing matter more than technical labels.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The interview protocol and sample are both anchored in the author's PAI cases, making the reported themes potentially an artifact of that design rather than a generalizable field consensus.","rationale":"The reader's weakest_assumption flagged sample representativeness, and the rationale also mentioned circularity. I focus on the combined design dependency because it is the most load-bearing condition for the central claim: to infer that synthetic media governance should prioritize misrepresentation and temporal perspectives, the themes must reflect stakeholder views beyond the author's own PAI network. Section 3.3 shows the interview guide itself was built from the author's autoethnographic themes from the same PAI cases used for sampling in Section 3.2, and Section 6 plus the Positionality Statement confirm the risk of network bias. A fresh, independent sample with a neutral protocol would settle whether the themes are robust or an artifact of selection and framing. The reader's UNVERDICTED verdict remains appropriate; the concern strengthens the case for withholding a definitive verdict until such a check is performed, but does not by itself change the verdict. If the replication test fails, the paper should be reconsidered as an exploratory case study rather than a generalizable finding.","tokens_in":16645,"tokens_out":6553,"duration_ms":70125,"concrete_test":"Run a pre-registered replication with a PAI-independent, global sample: recruit 30+ synthetic media stakeholders from Asia, Africa, and South America, and from sectors not involved in the Deepfake Detection Challenge or the Synthetic Media Framework. Interview them with open-ended prompts that avoid mentioning PAI, transparency, labeling, temporality, or the study's hypotheses (e.g., 'What are the main opportunities and risks of AI-generated media?'). Have two coders blind to the original themes independently code the responses. If the central themes (misrepresentation, temporal perspectives, trust, limits of technical transparency) do not emerge spontaneously at comparable rates, the original central claim is design-dependent and should be re-scoped as a case study of one multistakeholder community.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim that synthetic media governance should center misrepresentation and temporal perspectives rests on themes from 23 interviews, but the sample and instrument are both designed around the author's own PAI projects. Section 3.3 states the interview questions were 'drawn from themes that emerged during autoethnographic reflection on the PAI cases'; Section 3.2 recruited 13 participants from those very cases and used snowball sampling for 10 policymakers from Europe and North America. Section 6 concedes the Western focus and participants' pre-existing inclination toward multistakeholder approaches, and the Positionality Statement acknowledges the author's professional relationships with interviewees. This creates a self-confirming funnel: the researcher's priors are embedded in the protocol, participants are selected for exposure to those priors, and the analysis then rediscovers them. If this holds, the reported consensus on misrepresentation, temporal perspectives, trust, and technical-transparency limits describes the author's professional circle rather than the broader synthetic media governance field.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"This paper reports a qualitative study of multistakeholder synthetic media governance. The author draws on autoethnographic review of two Partnership on AI (PAI) cases (the Deepfake Detection Challenge and the Synthetic Media Framework), 13 interviews with participants in those cases, and 10 additional policymaker interviews from Europe and North America. Using semi-structured interviews and inductive coding, the paper identifies challenges (misrepresentation/impersonation, dual use, temporal perspectives), solutions (technical transparency with limits, moving beyond an 'AI or not' binary), and implementation factors (voluntary guidelines, trust, iterative governance). It claims these themes inform evidence-based policy design and contribute to the multistakeholder AI governance literature.","tokens_in":16768,"tokens_out":5702,"duration_ms":58984,"significance":"If the findings are robust, the paper provides rare first-hand empirical insight into how cross-sector actors actually collaborate on synthetic media governance, and it usefully situates the policy problem as misrepresentation rather than technology per se. The study has notable strengths: the full interview protocol is included in Appendix A; the methods and positionality are described in detail; the research received ethics approval; and Section 6 candidly acknowledges the Western sample and participants' pre-existing inclination toward multistakeholder approaches. The direct quotes give the analysis texture. However, due to the sample composition and the protocol's derivation from the same cases that recruited participants, the findings are best read as an exploratory insider case study rather than a representative field consensus.","major_comments":[{"comment":"The interview questions were explicitly 'drawn from themes that emerged during autoethnographic reflection on the PAI cases' (Section 3.3), and 13 of the 23 interviewees were participants in those same cases (Section 3.2). The protocol in Appendix A asks directly about dual use (Q7), transparency and disclosure (Q8), and multistakeholder collaboration (Q4/Q5), so the appearance of these themes in Sections 4.2.2 and 4.3 is partly by construction. Section 6 concedes that case participants were inclined toward multistakeholder approaches. This self-confirming design is load-bearing because the paper's central claim—that synthetic media should be governed as a problem of misrepresentation and impersonation with temporal and trust dimensions—rests on themes that the instrument itself seeded. I ask the author to address this by (a) reporting the codebook and any negative-case analysis, (b) using an independent second coder, or (c) reframing the contribution as an exploratory case study of insider perspectives rather than a general field consensus.","section":"Section 3.3 and Appendix A"},{"comment":"The sample is 23 individuals, of whom 13 came from the two PAI cases and 10 were policymakers recruited by snowballing from Europe (4) and North America (6). Sectoral composition is skewed: 7 industry, 3 civil society, 3 media, and 10 policy. There are no participants from the global South and no civil society participants outside the PAI orbit. Section 6 acknowledges these limits, but the Discussion (Section 5) nevertheless states that the research 'highlights synthetic media as a problem of misrepresentation and impersonation' without the caveat that this reflects a narrow group of stakeholders. The recruitment strategy therefore supports claims about this particular multistakeholder community, not about synthetic media governance broadly. Please either expand the sample or systematically qualify the general claims in Sections 4 and 5.","section":"Section 3.2, Table 1, Section 6"},{"comment":"The assertion that 'There was consensus among participants' on misrepresentation and impersonation as the central challenge is not backed by systematic evidence. The paragraph provides two illustrative quotes (PAI9 and PAI2), but no count of how many of the 23 participants expressed this view, how the code 'misrepresentation' was defined, or which participants dissented. Because this is the paper's headline finding, the analysis should quantify theme prevalence and show deviant cases. Otherwise the 'consensus' claim is unverifiable and could reflect selective quotation.","section":"Section 4.2.1"},{"comment":"The dual-use tally is internally inconsistent: the text reports '10 stakeholders... said it was net negative, four stakeholders (one media, two industry, two policy) said it was net positive, and eight either said it was too complicated'—the net-positive breakdown sums to five, and the three categories sum to 22 rather than 23. Please correct the counts and clarify whether one participant's response was uncodeable or missing. This is a reporting error in a results section that otherwise relies on counts sparingly.","section":"Section 4.2.2"}],"minor_comments":[{"comment":"The coding process would be easier to evaluate with a codebook including code definitions and example segments; the current description names attribute coding and inductive coding but does not specify how themes were counted or how code saturation was assessed.","section":"Section 3.4"},{"comment":"The table header and cells could be clearer: list the exact number of participants per sector in the header or a separate column, and ensure the participant IDs (PAI1, PAI 9, etc.) are formatted consistently.","section":"Table 1"},{"comment":"The statement that 'More than half of participants' referenced temporal perspectives should be accompanied by an exact count and a few representative quotes, matching the level of detail used elsewhere in the findings.","section":"Section 4.2.3"},{"comment":"Reference [29] lists the author as 'Claire R .' with an incomplete surname; this should be corrected.","section":"Reference list, [29]"}],"recommendation":"major_revision","confidential_remarks":"The paper is a qualitative governance study with a disclosed insider-researcher design. The central risk is the self-confirming relationship between the interview protocol, the sample, and the reported themes; the revisions must engage with that risk substantively rather than through an added limitation sentence. A reviewer with qualitative methods expertise would help assess whether the requested codebook, negative-case analysis, and reframing are sufficient. The paper fits the journal's scope, and the empirical material is potentially valuable if the claims are brought into proportion with the evidence."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"You should know this paper is worth a read, but read it as an insider account, not a neutral survey. The genuine contribution is the original empirical work: 23 interviews with people actually involved in PAI's two synthetic media governance efforts, plus ten policymakers. The thematic analysis is competent and the temporal-perspective angle—past, present, future shaping governance decisions—is a real addition to the AI governance literature. The paper is also unusually transparent: the limitations section and the positionality statement say plainly that the sample is Western, weighted toward people already inclined to multistakeholder approaches, and that the author led both case studies. That honesty is a mark in its favor.\n\nThe soft spot is the one the stress-test note names, and it is real. The interview questions are drawn from the author's own autoethnographic reflection on the same PAI cases, and 13 of 23 interviewees came from those cases. So the main themes—misrepresentation, the limits of technical labels, the importance of trust—are partly baked into the instrument and the sample. That does not make the findings false, but it does make them closer to 'what a well-informed professional circle believes' than to 'what the field has converged on.' The paper concedes this, but the framing in the abstract and discussion overstates how much the data can support. The policy interviews help, but they were also recruited from Europe and North America, so the Western-centric worry remains.\n\nI should be clear: I do not think this is a fatal flaw. The paper is an honest, well-documented qualitative study, and the findings are plausible and consistent with other work in the area. For a reader working on synthetic media policy or multistakeholder governance, it is a useful source of quotes, themes, and case detail. I would not cite it as evidence for a generalizable empirical claim, but I would cite it for its descriptive account of how PAI's processes actually worked.\n\nSend it to peer review. A serious referee should engage with it, mainly to push back on the generalization language and ask for a clearer separation between autoethnographic priors and interview findings. It is not desk-reject material; it is a revise-and-resubmit with a real chance of becoming a useful reference.","headline":"A candid insider qualitative study with real interview data and a genuinely useful temporal-perspective theme, but the design's circularity keeps the findings closer to a well-documented expert consensus than a field-wide result.","tokens_in":17287,"tokens_out":851,"would_cite":true,"duration_ms":12109,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"This paper argues that governing synthetic media is most productive when framed as a problem of misrepresentation and impersonation, not of AI technology alone, and that trust and time-awareness are the load-bearing governance resources.","keywords":["synthetic media","AI governance","multistakeholder governance","media transparency","deepfakes","misrepresentation","trust","qualitative interviews"],"falsifier":"A survey experiment in which audiences rate disclosures from trusted versus unfamiliar issuers could settle the paper's claim that trust in the disclosing authority is a prerequisite for transparency to work.","tokens_in":16407,"feed_emoji":"🎭","tokens_out":7741,"duration_ms":72175,"temperature":0.7,"pith_summary":"The paper studies how the people who actually govern synthetic media—civil society, industry, media, and policy actors—understand the problem and choose solutions. Based on 23 in-depth interviews and two real-world cases of multistakeholder governance, it argues that the core challenge is not the technology itself but misrepresentation and impersonation. It also finds that stakeholders' sense of time—past, present, and future—shapes their decisions, that trust among collaborators is a prerequisite for effective rulemaking, and that technical fixes like AI labels are necessary but limited. The stakes are practical: if the paper is right, governance efforts that center on \"was this made by AI?\" will keep missing the point, and policy should instead supply context, build trust, and adapt over time.","feed_headline":"AI labels are not enough for synthetic media trust","feed_subtitle":"23 stakeholders across policy, industry, media, and civil society say context and trust matter more.","key_machinery":"The analytical machinery is a qualitative, inductive thematic analysis of 23 semi-structured interviews, anchored in two real-world governance case studies in which the author participated. Three coding passes—attribute coding, descriptive coding, and higher-level thematic coding—yielded the organizing concepts that carry the argument: a three-stage model of governance (understanding the problem, developing norms, implementing solutions), a dual-use account of synthetic media, the temporal perspective spanning past, present, and future as a mediating frame, and trust as both an interpersonal precondition and an audience-facing property of transparency interventions. The temporal lens does the heaviest interpretive work: it reconciles the seemingly contradictory view that synthetic media is old news with the view that it demands urgent, novel governance.","core_discovery":"The paper's central finding is that the synthetic media challenge, as the stakeholders who govern it describe it, is a problem of misrepresentation and impersonation rather than a problem of AI-generated content as such. That framing carries three corollaries. First, technical transparency measures such as provenance metadata and \"AI or not\" labels are worth pursuing but cannot carry the governance burden alone, because knowing whether a machine touched an image says nothing about whether the content misleadingly represents someone or something. Second, trust does double work: audiences must trust the disclosures they are given, and the diverse institutions writing those disclosures must trust one another enough to move past corporate positions. Third, time matters: stakeholders frame synthetic media as an evolution of an old problem, yet feel a new urgency because harms appear faster than beneficial uses, and their governance recommendations emphasize iterative, adaptable rules.","pith_inferences":["If temporal framing does the interpretive work the paper claims, a comparative study of earlier visual-manipulation governance moments, such as Photoshop-era photo ethics, should find the same past-present-future pattern; that would test the mechanism outside synthetic media.","The paper's trust finding suggests a testable extension: audience trust in a transparency label should vary with the credibility of the issuing institution, so a label from a platform with a commercial interest may need to be relayed through a third party to work.","Because the sample skews Western and toward participants already sympathetic to multistakeholder governance, the author's implicit claim that misrepresentation is the unifying problem may not hold for non-Western regulatory contexts, where state surveillance, labor displacement, or cultural production harms could dominate the agenda."],"forward_implications":["Governance metrics should track reductions in misrepresentation and impersonation, not just whether content carries an AI label.","Policymakers should pair technical transparency with media literacy and contextual disclosures such as source, editorial changes, and process, rather than stopping at a binary \"AI or not\" marker.","Voluntary multistakeholder guidelines can catalyze alignment and inform regulation, but they need eventual enforcement to be effective.","Trust-building among stakeholders should be treated as a first-order governance activity, since it enables honest collaboration on hard problems.","Rules should be designed to be revisited frequently, because stakeholders expect both the technology and public understanding of it to change quickly."],"supporting_citations":[{"why":"supplies the definition of synthetic content that the paper adapts for its scope","marker":"[8]"},{"why":"anchors the first real-world governance case, the deepfake detection competition","marker":"[12]"},{"why":"supplies the landscape of AI principles that motivates multistakeholder governance","marker":"[15]"},{"why":"provides the definition of multistakeholder process the paper adopts","marker":"[24]"},{"why":"the author's earlier report on the first case, source of starting themes for interviews","marker":"[29]"},{"why":"the responsible-practices framework that constitutes the second governance case","marker":"[40]"},{"why":"supplies the attribute and inductive coding method used to derive themes","marker":"[48]"},{"why":"provides evidence on how transparency interventions are commonly misunderstood, which the interviews engage","marker":"[49]"},{"why":"supplies the analysis of promises and perils of AI content labeling that the findings extend","marker":"[58]"}],"fun_headline_variants":["Synthetic media trust hinges on context, not just AI labels","23 stakeholders: labels alone won't ensure synthetic media trust","For synthetic media, trust outweighs transparency labels, say experts","Study: synthetic media needs trust-building, not just labels","Multistakeholder governance: synthetic media needs more than labels"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The argument's load-bearing premise is that the 23 people interviewed, mostly from North America and Europe and many from the author's own professional network and sympathetic to multistakeholder governance, are representative enough to reveal the field's shared understanding of synthetic media.","fun_headline_variants_meta":{"raw":{"variants":["Synthetic media trust hinges on context, not just AI labels","23 stakeholders: labels alone won't ensure synthetic media trust","For synthetic media, trust outweighs transparency labels, say experts","Study: synthetic media needs trust-building, not just labels","Multistakeholder governance: synthetic media needs more than labels"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000935,"raw_usage":{"total_tokens":4003,"prompt_tokens":954,"completion_tokens":3049,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":570,"completion_tokens_details":{"reasoning_tokens":2965}},"tokens_in":570,"tokens_out":3049,"duration_ms":22714,"temperature":1.0,"reasoning_tokens":2965,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-08T22:23:12.534681+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"A survey experiment in which audiences rate disclosures from trusted versus unfamiliar issuers could settle the paper's claim that trust in the disclosing authority is a prerequisite for transparency to work.","supporting_citations":[{"cited_title":"The Journal of Politics (September 2024)","cited_arxiv_id":null,"evidence_quote":"supplies the definition of synthetic content that the paper adapts for its scope"},{"cited_title":"NIST (November 2024)","cited_arxiv_id":null,"evidence_quote":"anchors the first real-world governance case, the deepfake detection competition"},{"cited_title":"governance modalities and governance functions","cited_arxiv_id":null,"evidence_quote":"supplies the landscape of AI principles that motivates multistakeholder governance"},{"cited_title":"What’s changed? MIT Technology Review","cited_arxiv_id":null,"evidence_quote":"provides the definition of multistakeholder process the paper adopts"},{"cited_title":"From Principles to Practices: Lessons Learned from Applying Partnership on AI's (PAI) Synthetic Media Framework to 11 Use Cases","cited_arxiv_id":"2407.13025","evidence_quote":"the author's earlier report on the first case, source of starting themes for interviews"},{"cited_title":"Twitter Blog","cited_arxiv_id":null,"evidence_quote":"the responsible-practices framework that constitutes the second governance case"},{"cited_title":"IEEE Journal of Selected Topics in Signal Processing 14, 5 (August 2020), 910—932","cited_arxiv_id":null,"evidence_quote":"supplies the attribute and inductive coding method used to derive themes"},{"cited_title":"The Verge","cited_arxiv_id":null,"evidence_quote":"provides evidence on how transparency interventions are commonly misunderstood, which the interviews engage"}],"review_version":1}