The piece circulating with the headline "How generative AI is growing and the risks to Iowans" captures a real — and increasingly urgent — set of trends playing out across Iowa: rapid adoption of generative AI tools, rising incidents of deepfakes and AI-driven misinformation, legal and educational harms tied to hallucinated content, and an emerging patchwork of state-level controls and guidance intended to limit damage while preserving utility. While the specific Des Moines Register URL you shared could not be fetched by automated crawlers at the time of verification, its reporting themes are consistent with multiple independent local and national reports and official policy actions documenting real harms, warnings, and policy responses in Iowa.
Background
Generative AI — systems that can produce text, images, audio and video on demand — moved in 2023–2025 from novelty to infrastructure. Across the United States, state governments, school districts and public-safety offices have been forced to confront immediate harms such as non-consensual deepfakes, fabricated legal citations, automated disinformation, and AI-assisted cyberattacks. Iowa is no exception: state agencies have issued policies and warnings, school systems have updated rules for student and staff use, and law-enforcement and judicial actors are dealing directly with evidence and filings that originated with or were manipulated by AI.
- The State of Iowa published an enterprise generative AI policy intended to set minimum requirements and prohibited uses for agency staffers and contractors.
- The Iowa Attorney General issued warnings to parents and schools about deepfake images and videos being used to bully and harm students. Multiple local broadcast outlets reported on the AG’s warnings and linked them to a dramatic rise in reports to national centers.
- Judges and courts are encountering attorney filings that cite fabricated cases or otherwise rely on AI-generated assertions that do not withstand verification; coverage by regional outlets highlights real sanctions and discipline risks.
At the same time, industry-level research and forensic audits show that multimodal assistants (those that accept images as inputs and return provenance statements) are frequently unreliable when asked to confirm whether a photo or video is genuine. Independent audits and academic studies find repeated failure modes: confident but incorrect provenance statements, fabricated bibliographic citations, and misattribution of imagery. These technical weaknesses are the engine behind many of the harms now surfacing in Iowa and elsewhere.
Is the Des Moines Register article "real"?
What was directly verifiable
Automated verification systems were unable to fetch the exact Des Moines Register page at the URL you supplied because that publication blocks certain crawlers (robots.txt) and paywalled pages can prevent third‑party access. That means an automated tool could not retrieve the article text directly, but that does not in itself indicate the article is fake. The Des Moines Register is a well‑established local outlet that routinely covers technology, education and law-enforcement developments; a December 31, 2025 tech feature about generative AI and Iowa would be wholly consistent with the paper’s coverage. However, when a specific article URL cannot be crawled, best practice is to corroborate the reported facts with independent sources rather than rely solely on the single link.
What corroborates the article’s central claims
The themes the headline describes —
growth of generative AI in Iowa and
risks to Iowans — are corroborated by multiple independent public records and news stories:
- The State of Iowa’s official generative AI policy (enterprise interim policy, effective March 31, 2025) documents that Iowa government is actively regulating how state workers use generative AI and enumerates prohibited uses. This is an official policy action that supports the claim Iowa is taking AI seriously at the state level.
- The Iowa Attorney General’s office publicly warned parents and schools about the rise in deepfakes targeting students; local TV stations and news sites reported the AG’s statement and referenced a very large increase in National Center for Missing & Exploited Children (NCMEC) CyberTipline reports involving generative AI. Those reports materially back up concerns about real-world harms to young people.
- Judicial and legal reporting shows judges and disciplinary bodies increasingly encountering AI‑generated fabrications: regional reporting documented attorneys citing cases that do not exist and courts imposing sanctions or requesting corrective action. That dynamic explains why the legal community is a frequent subject in coverage about AI risks to residents.
Taken together, these independent sources make the article’s core thrust — that generative AI is growing in use and producing tangible harms in Iowa — verifiable and credible, even if the specific Des Moines Register piece could not be retrieved automatically. Where the Register’s article adds local detail or particular anecdotes, those should be checked directly against the newspaper’s own copy or via a direct browser session behind any paywall.
What the reporting gets right: documented harms and trends
Deepfakes and student safety
There has been a documented surge in the use of AI to create non-consensual sexual images and other deepfakes targeted at students. The Iowa Attorney General’s warning reflected both a moral panic and an empirically grounded increase in reports to national child protection hotlines. The scale is non-trivial: public reporting cites very large percentage increases in CyberTipline referrals implicating generative AI, and Iowa authorities rightly flagged the juvenile‑safety and legal consequences. Why this matters locally: schools often lack the forensic capacity to trace the origin of a manipulated image, and victims can suffer long-lasting social and legal harm. Practical responses being adopted in some Iowa districts — vetting AI tools, prohibiting certain uses, and educating students and parents — are sensible first lines of defense.
Hallucinations in legal filings and professional risk
Generative language models regularly invent plausible‑looking but false details —
hallucinations — including fictitious case law or mis‑sourced citations. Courts outside Iowa have sanctioned lawyers for filing briefs that relied on AI‑invented decisions; regional reporting shows Iowa lawyers and courts are confronting similar problems. This is not a speculative worry: it is already affecting courtroom practice and professional liability. Practical consequence: lawyers, judges and court clerks must treat AI‑assisted research as preliminary and require human verification before relying on generated citations in filings.
Misinformation, viral fakes and multimodal failures
Multimodal assistants that accept images can sometimes
both generate fake images and then — when asked — incorrectly assert those images are authentic. Independent audits have shown high failure rates for provenance verification across mainstream assistants, creating a nasty feedback loop: AI generates a forged image; users ask an assistant if the image is real; the assistant returns an authoritative but incorrect assessment. That failure mode explains many viral misinformation episodes.
Infrastructure and systemic risk
Beyond individual incidents, industry reporting and independent analyses warn of systemic vulnerabilities: concentration of model access in a handful of providers, energy and data center demands, and the rise of agentic systems that can call APIs and perform tasks autonomously. These are long‑term concerns with concrete short-term manifestations: cyber‑incidents using AI orchestration, new regulatory obligations for vendors, and reshaped labor markets. Those broader trends are the context for why Iowa — like other states — is moving to set policy guardrails.
Where reporting tends to overreach — and what to watch for
Sensational anecdotes vs. systematic evidence
Local headlines often favor vivid anecdotes (the viral T‑shirt‑cannon TikTok clip, a student deepfake, a lawyer’s bogus citation) that make dangers tangible. Those anecdotes are real and useful, but they do not always establish systemic causality. Reliable policy requires repeated, audited incidents and reproducible tests. When a story implies a single viral event proves a sweeping national conspiracy or technical inevitability, treat that claim skeptically. Independent audits, not viral posts, should shape rules for critical infrastructures or courts.
Attribution gaps
Tracing a viral image or video back to a specific AI tool or model is often technically challenging or impossible. Metadata may be stripped, and multiple generative pipelines can produce similar artifacts. Journalists should be cautious when attributing a fake to a named model without reproducible forensic evidence. Where a story names a specific model as the generator, expect to see corroborating forensic detail. If that detail’s absent, label the attribution as unverified.
Numbers that need context
Percentages like "1,325% increase in CyberTipline reports" are accurate in context but can mislead without baseline figures. A small initial numerator can create a dramatic percentage change. Reporting should include absolute numbers where possible and clarify time windows and data sources. Multiple local outlets reporting the same statistic strengthens credibility, but exact policy decisions should be based on complete datasets (not single snapshots).
Practical guidance for Iowans — measured, realistic steps
For parents, teachers and school districts
- Treat deepfakes and non‑consensual AI images as a serious safety issue: document incidents, report to law enforcement and the NCMEC, and preserve any metadata you can.
- Implement clear, published policies on student use of generative AI in coursework and exams; require disclosure and verification for AI-assisted submissions.
- Invest in digital‑literacy training for students and staff focused on spotting deepfakes and verifying sources.
For lawyers and judges
- Assume AI output is a draft only. Require human verification before filing or citing.
- Maintain an audit trail: save prompts, outputs, timestamps, and citations used to arrive at legal conclusions.
- Train court clerks and judges to spot common hallucination patterns and to require proof of source for novel citations.
For local government and agencies
- Update procurement policies to specify model provenance, data‑residency guarantees and audit rights.
- Use the state’s enterprise AI policy as a baseline; require vendors to provide provenance and human‑review mechanisms for any AI used in public service delivery.
For everyday users and social media consumers
- Maintain a skeptical verification habit: before resharing, use reverse image search, look for original reporting, and ask whether a plausible provenance exists.
- Remember that AI assistants can be wrong — ask for sources and verify them independently.
Policy, technology and the tradeoffs ahead
Policy directions Iowa (and other states) are pursuing
Iowa’s enterprise policy is representative of a pragmatic state-level approach: restrict risky uses, require human accountability, and create procurement guardrails. Other possible policy moves — from labeling requirements for AI‑generated content to criminal enforcement for malicious deepfakes — are politically and technically complicated. Policymakers must avoid knee‑jerk bans that stifle safety research while building enforceable transparency and audit frameworks.
Technical fixes are necessary but insufficient
Technical mitigations — model watermarks, provenance metadata, dedicated forensic detectors — can reduce risk but are not silver bullets. Audits show that many detectors lag generators; adversaries adapt quickly. The safest near‑term posture is a layered one: provenance APIs from vendors, independent forensic tools in newsrooms and courts, and human‑in‑the‑loop verification for high‑stakes decisions.
Economic and social tradeoffs
Generative AI brings real productivity gains — in drafting, research, design and diagnostics. At the same time, automation pressure and misclassification risks will require new reskilling and stronger institutional safeguards. For Iowa, where agriculture, manufacturing and education are major sectors, targeted training and measured infrastructure investments (data centers, workforce programs) can capture benefits while limiting harms.
How the reader can judge whether a specific article is "real" or reliable
When you encounter a headline like the one you shared, apply a simple verification checklist:
- Can the article itself be accessed directly (not behind an unknown paywall) and does it include named sources, documents or interviews?
- Are the central factual claims corroborated by at least two independent outlets or official documents (for example, a government policy, an AG press release, or a court filing)?
- Does the article distinguish anecdote from data and include absolute numbers (not only percentages) when citing trends?
- Are technical claims verified against independent technical analyses, audits or peer‑reviewed studies rather than only vendor statements?
If the article meets these tests it is more likely to be a reliable report; if it fails one or more, treat specific claims as provisional until confirmed. In the case of the Des Moines Register link you provided, the central themes are corroborated by official state policy documents and multiple local reports; the Register’s specific examples or phrasing should be checked by viewing the article directly (or contacting the paper) where possible.
Closing analysis: strengths, risks and an evidence-led path forward
The core strength of the reporting (in the Register and in other local outlets) is that it brings a local focus to a global technical reality: generative AI’s harms are not abstract — they are being experienced in schools, courtrooms and community life in Iowa today. Local reporting helps translate national trends into actionable local policy. At the same time, the most important weakness in much popular coverage is occasional slippage from documented incidents to broad attribution claims that are hard to substantiate technically.
- Strengths to preserve:
- Localized investigations that surface real victims and concrete regulatory responses.
- Pressure on public institutions to adopt defensible policies and training.
- Public awareness that prompts legislative and administrative action.
- Risks to mitigate:
- Over‑attribution of harm to a single vendor or model without forensic proof.
- Panic-driven policies that block useful tools for education or public service without offering safe alternatives.
- Failing to invest in independent audits, forensic capability and cross‑sector coordination.
The responsible path forward for Iowa — and for any community confronting similar risks — is evidence led: pair immediate protective measures (education, mandatory disclosure, human verification) with investments in forensic tools, mandatory vendor transparency for government contracts, and multi‑stakeholder audits that include independent technical reviewers. That balanced approach preserves the productivity gains of generative AI while limiting the most acute risks to schools, courts and civic life.
The particular URL you shared could not be fetched by automated tools because the publisher’s site restricts crawler access, but the article’s claims about generative AI growth and risks to Iowans are supported by state policy documents, Attorney General statements and multiple independent news reports documenting deepfakes, hallucinations in legal filings, and active local policy responses. Read the Register piece directly behind its site or paywall for local examples and quotations; use the verification checklist above to evaluate any single assertion, and prioritize human verification for any AI‑derived content that has legal, educational or safety consequences.
Source: The Des Moines Register
https://www.desmoinesregister.com/s...-ai-iowa-research-spot-the-fakes/87551266007/