pith:CZQYIROW
Fairness-Aware Multi-Group Target Detection in Online Discussion
A fairness-aware approach for detecting multiple target groups in social media posts reduces bias across demographic groups while maintaining strong predictive performance.
arxiv:2407.11933 v6 · 2024-07-16 · cs.LG
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\usepackage{pith}
\pithnumber{CZQYIROWIAIAHK7KRLO4MRPIVA}
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Record completeness
Claims
We show our fairness-aware multi-group target detection approach both reduces bias across groups and shows strong predictive performance, surpassing existing fairness-aware baselines.
The fairness constraints and evaluation metrics used accurately reflect real-world fairness requirements in toxicity detection across demographic groups (implied by the abstract's framing of the problem and results).
A fairness-aware multi-group target detection approach reduces bias across groups and outperforms existing fairness-aware baselines in toxicity detection tasks.
Receipt and verification
| First computed | 2026-06-19T16:12:44.497218Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
16618445d6401003abea8addc645e8a80611306dade4677f89bd9a4962c5519a
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/CZQYIROWIAIAHK7KRLO4MRPIVA \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 16618445d6401003abea8addc645e8a80611306dade4677f89bd9a4962c5519a
Canonical record JSON
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"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.LG",
"submitted_at": "2024-07-16T17:23:41Z",
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"source": {
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"kind": "arxiv",
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