W4363643755 Peer-reviewed research AI × management and organizations

Adverse impact reduction and job performance optimization via pareto-optimal weighting: A shrinkage formula and regularization technique using machine learning.

Q. Chelsea Song · Chen Tang · Daniel A. Newman · Serena Wee

Also recorded as: doi:10.1037/apl0001085

Journal of Applied Psychology · 2023-09-01

Source of record — doi.org See it on the corpus view

Recorded claims: 1
Evidence classes: 1
Recorded limitations: 2

From the hash-bound evidence releases

Recorded claims

Each claim states what the inspected source says, at the recorded location—bounded by its scope and graded by its confidence. Nothing here is a synthesis across works.

The title identifies a machine-learning weighting method aimed at reducing adverse impact while optimizing job performance.

confidence: moderate crossref-api-metadata apw-c1-obot-journals

Scope: title-level

Structured relationships

In-corpus citations

Only source-supplied cites relationships whose two endpoints are admitted are shown. Invocation evidence remains a separate relationship.

Cites (0)

No in-corpus outgoing citation is available in the current enrichment coverage.

Cited by (1)

Whither bias goes, I will go: An integrative, systematic review of algorithmic bias mitigation.

Boundaries

Limitations & independence

Recorded at coding time, carried with the work forever. A claim without its limits is not evidence.

Recorded limitations

Crossref title metadata only; an abstract was not present in the retained response and full text was not inspected.
The publisher surface was not queried after the lane’s quarantined dynamic-challenge response; no independent publisher-page verification was attempted.

Source independence

publisher-only