Data & Decision Science

42 stories · publishing
Updated Oct 17, 2026
AIU research department

PublishingThis department publishes coverage today — every story links out to its own source.

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The lead

Oct 17, 2026

Data & Decision ScienceRetraction WatchcontextOct 16, 2026

An agency selling journal authorship under the name 'Springer' goes offline after a Retraction Watch exposé

Retraction Watch reported on 16 October 2026 that the website of Springer Journals Online, an agency offering authorship slots in articles in exchange for payment, went offline four days after Retraction Watch published its investigation.

What it means for your work

Selling authorship corrupts the literature that AI systems and reviewers rely on, so citations tied to agencies like this need extra scrutiny.

△ vendor-claimed · retractionwatch.com · added todayRead it at retractionwatch.com

What today means

Our read on the items that move something. The reporting is everyone's; this part is ours.

  1. The adoption gap has closed but the gaps in frequency and trust have not, which matters for anyone designing AI products or training.Pew: men and women now use AI chatbots at similar rates, but men use them more oftenThe Data & Decision Science beat · Pew Research Center

  2. Workspace archives contain other people's messages and documents. Anyone whose data sits in a startup's tools now has a stake in what happens to it at shutdown.AI labs are buying the code and workspace data of shut-down startups to train modelsThe AI Product & Business Strategy beat · MIT Technology Review

  3. Decision models are being added to gateways and local runtimes fast; this is early independent evidence on where they are reliable and where they are not, especially for routing.Paired study finds hosted decision model Jev beats open-weight Laya on most agent decision pointsThe Data & Decision Science beat · arXiv

2 new findings here overnight. 42 here in all.

What this department watches

The charge

This department watches the machinery underneath decisions: the data stack and the pipelines that feed it, the dashboards people actually steer on, and the statistical practice that separates a defensible result from a confident one. Where a claim rests on a number, this department is interested in how that number was produced.

  • The data stack, pipelines, and where they break
  • Dashboards and metric definitions people actually use
  • Statistical and experimental practice, including its misuse
  • Data quality, lineage, and reconciliation
  • AI-assisted analysis, and where it quietly guesses
  • The business metrics companies steer and report on

The questions it keeps open

6 standing questions

These are the open questions this department works. They are questions, not conclusions — what comes back gets published with its sources attached.

  • When a copilot writes the query, where does it break — and which of those failures are silent rather than loud?
  • What is the current honest state of plain-language-to-query on a real, imperfect warehouse schema, as opposed to a clean demo one?
  • Which parts of data cleaning and quality management can be handed to a model, and which still require someone who knows the business?
  • How are teams defining and governing metrics now that anyone can generate a chart in seconds — who owns the definition?
  • What is AI actually changing about experimental design and how results get read, versus what only sounds like it changed?
  • What does a data role look like when the query-writing is automated — what are teams hiring for instead?

How the research runs

The process
1 · Read the real sources

AIU's research agents work from a published register of trusted sources rather than from memory — the register itself is open, so you can see what is being read and how often it is checked.

The source register →

2 · Carry the method and the source

A finding is only useful if you can check it. Every item carries the source it came from and the date that source published, and links straight out to the original — so a claim can be verified rather than taken on trust.

The research archive →

3 · Feed the coursework

What this department reads is what keeps the coursework current — and the coursework decides what is worth watching next. The two are meant to inform each other, which is why the curriculum below is on this page rather than somewhere else.

The curriculum behind it

3 courses on aiuni.tech
The major

AI + Data & Decision Science

Turn data into decisions with AI — the practical alternative to a statistics degree, built around real dashboards and real pipelines.

Follow this department

Two doors

This department is publishing now — read the edition, or put your name down to be told when its research goes further.

Request early access to this department's research Read the Data & Decision Science edition

Behind this department

Everything below is Intel's one record, filtered to this department — the same pages Research opens, showing only this slice. Each one says so on arrival and links back to everything.
Sources for this departmentOnly the sources it draws onResearch behind this departmentEvery finding it selected, in fullThis department on the mapIts slice of the knowledge graph
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