A survey on provenance: What for? What form? What from?

331 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Bridging the Gap Between Ethics and Practice (2020, background): “Lessons from database systems [46] and information visualization [91] are useful.”
This Thing Called Fairness (2019, background): “The scientific interest in fairness, transparency, accountability, explainability, and other values in computer science has advanced the state of theory as well as application, engaging cutting edge research in cryptography [84], formal languages [1], and systems [63, 118].”
Trustworthy AI: From Principles to Practices (2021, background): “By tracking the data lineage at the highest resolution, data provenance can enhance the transparency, reproducibility, and accountability of an AI system [154, 172].”
Participatory provenance as representational auditing for AI-mediated public consultation (2026, unknown): “…and lineage of data through processing pipelines, is well-established in database systems [Bune-man et al., 2001, Simmhan et al., 2005, Herschel et al., 2017], but has not been adapted to the specific challenge of tracking how individual citizen voices are transformed through…”