Pith. sign in

REVIEW 1 cited by

Identifying the Prevalence of Gender Biases among the Computing Organizations

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2107.00212 v1 pith:WW6ERFC6 submitted 2021-07-01 cs.SE

Sayma Sultana , London Ariel Cavaletto , Amiangshu Bosu This is my paper · ORCID
classification cs.SE
keywords biasesgendercomputingorganizationsalmostcontemporarydesigneddimensions
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We have designed an online survey to understand the status quo of four dimensions of gender biases among the contemporary computing organizations. Our preliminary results found almost one-third of the respondents have reported first-hand experiences of encountering gender biases at their jobs.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts

    cs.SE 2025-01 conditional novelty 6.0 of 10

    An automated linting and repair tool finds 3.46% of developer prompts biased, 10.75% injection-vulnerable, and improves a fraction of suboptimal prompts.

Pith tools