{"paper":{"title":"Fairness-Aware Multi-Group Target Detection in Online Discussion","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A fairness-aware approach for detecting multiple target groups in social media posts reduces bias across demographic groups while maintaining strong predictive performance.","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Maria De-Arteaga, Matthew Lease, Soumyajit Gupta","submitted_at":"2024-07-16T17:23:41Z","abstract_excerpt":"Target-group detection is the task of detecting which group(s) a piece of content is ``directed at or about''. Applications include targeted marketing, content recommendation, and group-specific content assessment. Key challenges include: 1) that a single post may target multiple groups; and 2) ensuring consistent detection accuracy across groups for fairness. In this work, we investigate fairness implications of target-group detection in the context of toxicity detection, where the perceived harm of a social media post often depends on which group(s) it targets. Because toxicity is highly con"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"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).","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A fairness-aware multi-group target detection approach reduces bias across groups and outperforms existing fairness-aware baselines in toxicity detection tasks.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A fairness-aware approach for detecting multiple target groups in social media posts reduces bias across demographic groups while maintaining strong predictive performance.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"8a38e67da924df0530aad0250155102c2fe0921aa53ddf235295a8c3920234cc"},"source":{"id":"2407.11933","kind":"arxiv","version":6},"verdict":{"id":"d480490a-a999-4e3c-a1af-defcabd9492d","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-23T22:38:15.864448Z","strongest_claim":"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.","one_line_summary":"A fairness-aware multi-group target detection approach reduces bias across groups and outperforms existing fairness-aware baselines in toxicity detection tasks.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"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).","pith_extraction_headline":"A fairness-aware approach for detecting multiple target groups in social media posts reduces bias across demographic groups while maintaining strong predictive performance."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2407.11933/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}