Eric Frydenlund’s recent column arguing that “AI lacks the authenticity of human experience” landed as both a provocation and a practical warning: provocative because it challenges a booming tech narrative that smarter models equal better judgement, and practical because the failures Frydenlund highlights — in tone, provenance and moral judgment — are already shaping how people read, trust, and use digital content. s://muckrack.com/eric-frydenlund)
For WindowsForum readers — system builders, IT leads and community journalists — the takeaway is straightforward and urgent: embrace AI’s practical power for triage and productivity, but institutionalize the practices that protect authenticity — human oversight, provenance logging, calibrated uncertainty, and cross‑validation. Doing otherwise mixes the convenience of automation with the moral hazard of abdication; in an era when a single bot reply can shape public discourse, that is a risk no newsroom or community should take lightly.
Source: TelegraphHerald.com Frydenlund: AI lacks authenticity of human experience
Background
Where the debate began (and who said it)
Eric Frydenlund, a regular columnist whose work appears in regional outlets, framed his piece as more than opinion; it is a call to reassess what generative systems can and cannot be — not merely what they can do. Frydenlund’s critique echoes a larger body of commentary and research that treats AI’s fluency as an insufficient stand‑in for judgment.Why this matters now
Generative AI is not a lab curiosity anymore. It is embedded in operating systems, productivity suites, search assistants and news feeds. That scale means a single confident answer from a conversational assistant can substitute for multiple human checks. When those assistants are wrong, the consequences are no longer hypothetical. Journalistic audits and academic tests repeatedly show that assistants can be brilliant at mirroring human language while being significantly less reliable at y, sourcing, or emotional intentionality.Overview: Frydenlund’s core argument
Frydenlund’s piece lands three core claims:- AI produces convincing outputs but often lacks the lived texture that gives human writing authenticity.
- That gap matters in high‑stakes settings — journalism, therapy, caregiving, and civic discourse — where authenticity is part of credibility.
- The response should not be technophobic rroduct design, editorial practice, and regulation to ensure AI is used as an assistant, not an arbiter.
The anatomy of “authenticity”: what humans bring that AI does not
1) Embodiment and lived experience
Human authenticity is built on embodied memory — sensory, social and moral histories that inform choices of metaphor, omission, and emphasis. AI models, by contrast, do not possess an embodied vantage point: they have no sensory life, no personal stakes, and no phenomenological continuity. Scholars describe this as an absence of subjectivity or experiential grounding; it’s why an AI can describe grief convincingly in the abstract but stumble when a passage must signal the messy, contradictory nature of real sorrow.2) Moral agency and intentionality
Authentic human expression often signals intent: an apology that hesitates, a brag that reveals insecurity, a memory that betrays a bias. AI has no intentions; it simulates them. This matters when readers rely on language to infer motives. Research shows people penalize AI authorship in emotionally sensitive contexts; disclosure that a message was produced by AI reduces perceived sincerity and moral presence.3) Error, risk and voice
Paradoxically, human authenticity often includes error and risk. A flawed metaphor, a surprising digression, or a blunt local reference can reveal authorship. AI, trained to optimize for fluency and safety, flattens those edges. The result: polished but anonymous prose that reads like a strong template rather than a person. Readers detect that flattening — sometimes as a vague “uncanny” sensation — and penalize it.Evidence from audits and studies
Misclassification and provenance failures
Beyond stylistic a concrete, documented problem with provenance — the ability to say whether an image or claim is genuine. Multiple journalist‑led audits have shown assistants confidently misclassifying AI‑generated images as authentic photographs, sometimes amplifying a false narrative before corrections can catch up. Those audits conclude that while multimodal assistants are powerful for triage and discovery, they are not yet reliable certifiers of authenticity.Emotional trust studies
Experimental work in psychology finds a double‑edged effect: AI can produce feelings of closeness in short interactions, often because it overshares or mirrors the user’s disclosures. But when people learn their partner is an AI, feelings of trust and perceived sincerity drop, especially in contexts demanding moral or emotive authenticity. That pattern shows how simulation can produce short‑term engagement while undermining longer‑term perceived authenticity.Academic convergence
Frontiers and other peer‑reviewed venues increasingly document the same diagnosis: AI’s fluency masks a deeper absence of the features we use to ascribe agency and worth to communication — features that matter for ethialism. These studies support Frydenlund’s qualitative claim with quantitative and experimental evidence.Strengths and real value: what Frydenlund acknowledges (and what the research confirms)
It would be wrong to paint Frydenlund’s stance as Luddite rejection. His piece, and the literature behind it, recognize concrete ways AI is already valuable:- Rapid triage and summarization at scale: AI accelerates discovery, surfacing leads and metadata that human teams can follow up.
- Productivity gains in mundane tasks: drafting, formatting, and pattern detection free humans to focus on higher‑value judgment.
- Accessibility and personalization benefits in low‑stakes contexts: conversational agents can make information easier to find for users with limited literacy or mobility.
Risks and systemic harms Frydenlund flags (and why they scale)
Erosion of trust in information ecosystems
When fluent assistants misattribute sources or assert provenance with unjustified confidence, misinformation accelerates. Corrections are slower and reach fewer people. That dynamic risks undermining public trust in both journalism and the platforms that surface content. Audits show that even a small proportion of high‑visibility misclassifications can cascade into significant reputational and civic harms.Devaluation of human creative labor
If enterprises systematically favor machine‑generated drafts because they are faster and cheaper, a monetizable market for authentic human voice shrinks. Frydenlund and others warn of a market dynamic that privileges algorithmic averages over idiosyncratic, risky, or deeply local creative work. Evidence from studies of readership perception suggests that authenticity — even when imperfect — remains valuable and often preferable to generic fluency. (angelabogdanova.com)Emotional harm in sensitive domains
Generative responses used in therapy, bereavement, or caregiving may feel satisfactory in the moment and yet lack moral grounding. Several psychology studies argue for caution in deploying AI for emotionally consequential communication without explicit human oversight and disclosure.tps://www.frontiersin.org/articles/10.3389/fpsyg.2025.1568911)Legal and regulatory exposure
As images and audio morph into convincingmoving toward disclosure and provenance regimes. Publishers and platforms that fail to adopt robust provenance practices could face consumer protection and defamation risks. not answer these questions; governance and audits must be part of the solution.Practical mitigations and product design recommendations
Frydenlund’s prnal, not purely rhetorical. The evidence and audits suggest a practical multi‑pronged response:- Enforce “humans in the loop” for high‑stakes outputs: never let a model’s unverified provenance claim stand as publication‑level evidence.
- Surface calibrated uncertainty: UI should show confidence bands, provenance metadata, and clear caveats when the assistant is speaking about authenticity.
- Ensemble veneralist multimodal assistants with specialized forensic detectors and cross‑platform corroboration rather than trusting a single model.
- Mandatory provenance logging: preserve inputs, model versions, and retrieval artifacts to enable audits. This is both a product design and a compliance measure.
- Disclosure and labeling: for emotionally significant or editorial content, label AI involvement clearly and provide route to human escalation. Research shows disclosure affects perceived authenticity and trust.
What Windows users and community journalists should do today
If you run a newsroom, manor depend on Copilot‑style assistants in your workflow, here are concrete steps you can take immediately:- Require a human sign‑off on any content that asserts provenance or makes reputational claims.
- Log model inputs and outputs for a minimum retention window; keep tamper‑evident audit trails.
- Train staff to treat AI outputs as leads not answers: verify original sources and metadata before publishing.
- Use multiple independent detectors when assessing images or audio; cross‑validate rathle assistant.
- Be explicit with readers: label AI‑assisted summaries and provide links or references to underlying reporting whenever possible.
Where Frydenlund is strongest — and where his argument needs nuance
Strengths
- Frydenlund correctly centers authenticity as a functional quality, not a sentimental one. Authenticity affects credibility, legal exposure and civic outcomes. That reframing helps move the debate from abstract ethics to concrete operational risk.
- He aligns with cross‑disciplinary evidence: the verdicts of journalism audits and experimental psychology studies converge on the same diagnosis.
Areas needing nuance
- Frydenlund’s framing can read as a wholesale condemnation of all AI use in expressive roles, which risks discarding useful tools in low‑stakes contexts. The evidence supports a calibrated approach: speed; humans remain necessary where nuance and accountability matter.
- Not all “authenticity” can be perfectly operationalized. There are trath, coverage vs. provenance. Public policy and product design must weigh these trade‑offs transparently rather than assume a single right answer.
Longer‑term technical and policy directions
Looking past immediate mitigations, the research and audits point to three sustained priorities:- Purposeful forensic datasets and detection objectives: model training regimes should include provenance and detection tasks, not just generation. This helps narrow the mismatch between generative objectives and forensic needs.
- Regulatory standards for provenance: lawmakers and regulators should require minimum metadata standards for synthetic media, including signed provenance headers or watermarking schemes. This is already emerging as policy in several jurisdictions.
- Public funudit labs: neutral, recurring audits replicate journalistic efforts at scale and keep vendors accountable. Independent benchmarks help measure progress and avoid a never‑ending “arms race” of generator vs. detector.
A caution about unverifiable claims
Many public narratives about AI (especially early in press cycles) assert sweeping capabilities or imminent threats with insufficient evidence. Where Frydenlund or others cite specific prods should ask for concrete dates, model versions, and audit materials. Some high‑profile demonstrations lack reproducible prompts or preserved session logs; those should be treated as persuasive but not definitive evidence. Where claims rest on private telemetry or internal logs, independent verification must be sought.Conclusion — a pragmatic humanism for the AI age
Eric Frydenlund’s central claim — that current AI lacks the authenticity that cence — is less a romantic protest than a call for pragmatic humanism: design products that respect human judgment, build editorial workflows that require human certification, and regulate to preserve provenance. The literature and audits Frydenlund draws on show a consistent pattern: AI is superb at simulating the surface of human expression, but simulation is not substitution. That difference matters for trust, for legal risk, and for what we value in journalism and culture.For WindowsForum readers — system builders, IT leads and community journalists — the takeaway is straightforward and urgent: embrace AI’s practical power for triage and productivity, but institutionalize the practices that protect authenticity — human oversight, provenance logging, calibrated uncertainty, and cross‑validation. Doing otherwise mixes the convenience of automation with the moral hazard of abdication; in an era when a single bot reply can shape public discourse, that is a risk no newsroom or community should take lightly.
Source: TelegraphHerald.com Frydenlund: AI lacks authenticity of human experience
