W3039055470 Peer-reviewed research AI × management and organizations

When eliminating bias isn’t fair: Algorithmic reductionism and procedural justice in human resource decisions

David T. Newman · Nathanael J. Fast · Derek J. Harmon

Also recorded as: doi:10.1016/j.obhdp.2020.03.008

Organizational Behavior and Human Decision Processes · 2020-09-01

Recorded claims
2
coded from the inspected source
Evidence classes
1
crossref-api-metadata
Recorded limitations
5
stated, never hidden

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 work concerns procedural-justice implications of algorithmic bias reduction in human-resource decisions.

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

Scope: title-level metadata; abstract not inspected

The title identifies procedural-justice consequences of algorithmic reductionism in human-resource decisions.

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.

Boundaries

Limitations & independence

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

Recorded limitations

  • Crossref supplied bibliographic metadata but no abstract.
  • Scope and claim are limited to the title-level description.
  • Crossref supplied only year and month, so published_date is null rather than an inferred day.
  • 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