Data-Centric Artificial Intelligence
Data-centric artificial intelligence (AI) emphasizes the systematic enhancement of data to improve AI systems. This paradigm complements the existing model-centric approach, which focuses on refining models with a fixed dataset. The article aims to introduce this concept to practitioners and researchers in Business and Information Systems Engineering, highlighting its implications and tools available for implementation.
- ▪Data-centric AI focuses on improving the quality and quantity of data used in AI systems.
- ▪This new paradigm is intended to complement model-centric AI, which has traditionally dominated AI research.
- ▪The article provides a framework for understanding data-centric AI and its relevance to the Business and Information Systems Engineering community.
3 outlets in our directory ran this story, first to last over 10 hours. Coverage spans 2 points on the political spectrum — 1 lean left, 1 centre.
- ▪ No, Artificial Intelligence Is Not Conscious — The Atlantic
- ▪ Introduction to Data-Centric AI — Introduction to Data-Centric AI
Hacker News (Newest) files mainly under programming. We currently carry 5,306 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Springer |
| Canonical URL | https://link.springer.com/article/10.1007/s12599-024-00857-8 |
| Publication time | Wed, 03 Jun 2026 08:23:33 +0000 |
| Retrieval time | 2026-06-03T08:41:59.255Z |
| Last seen | 2026-06-03T08:41:59.255Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | Z_rhfUHXpBGJ · 3 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Home Business & Information Systems Engineering Article Data-Centric Artificial Intelligence Catchword Open access Published: 05 March 2024 Volume 66, pages 507–515 (2024) Cite this article You have full access to this open access article Download PDF Save article View saved research Business & Information Systems Engineering Aims and scope Submit manuscript Data-Centric Artificial Intelligence Download PDF Johannes Jakubik1, Michael Vössing1, Niklas Kühl2, Jannis Walk1 & …Gerhard Satzger1 Show authors 13k Accesses 92 Citations 14 Altmetric 2 Mentions Explore all metrics AbstractData-centric artificial intelligence (data-centric AI) represents an emerging paradigm that emphasizes the importance of enhancing data systematically and at scale to build effective and efficient AI-based…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Springer.
