{"as_of":"2026-08-11T14:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d41a51af751ac86a1f2377d0ff0f5e6b664cf4e0a8a1d664450379dc6c87dbd9","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T22:53:26.655644Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-12T07:42:05.609805Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1707.00075","last_updated":"2017-07-07T01:31:36Z","snapshot_observed_at":"2026-07-06T05:49:19.425378Z","submitted_at":"2017-07-01T01:09:33Z","title":"Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.00075","snapshot_observed_at":"2026-08-10T22:53:26.655644Z","title":"Beutel, J","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.00595","last_updated":"2024-12-31T18:48:30Z","snapshot_observed_at":"2026-08-10T22:44:49.068443Z","submitted_at":"2024-12-31T18:48:30Z","title":"Unbiased GNN Learning via Fairness-Aware Subgraph Diffusion","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T22:53:26.655644Z"},"links":{"cited_paper":"/paper/1707.00075","citing_paper":"/paper/2501.00595"},"observation_digest":"sha256:24d0dda06aa2d55636af5105228921c0160be6eed886b3ea964234c69ad9187c","observation_id":"35a65bd6-baa8-4d28-a52e-ac9751914b95","resolution":{"observed_at":"2026-08-10T22:53:26.655644Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.00075","last_updated":"2017-07-07T01:31:36Z","snapshot_observed_at":"2026-07-06T05:49:19.425378Z","submitted_at":"2017-07-01T01:09:33Z","title":"Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.00075","snapshot_observed_at":"2026-08-10T20:35:25.680631Z","title":"Beutel, J","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.07885","last_updated":"2025-01-14T06:51:27Z","snapshot_observed_at":"2026-08-10T20:27:35.052393Z","submitted_at":"2025-01-14T06:51:27Z","title":"Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:25.680631Z"},"links":{"cited_paper":"/paper/1707.00075","citing_paper":"/paper/2501.07885"},"observation_digest":"sha256:82e29696e06fe74c2b8e311d87bfd30f87f82861a0debf8d99b61bfa54bec57b","observation_id":"e2191966-4b57-4049-9850-993c6ac4551a","resolution":{"observed_at":"2026-08-10T20:35:25.680631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.00075","last_updated":"2017-07-07T01:31:36Z","snapshot_observed_at":"2026-07-06T05:49:19.425378Z","submitted_at":"2017-07-01T01:09:33Z","title":"Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.00075","snapshot_observed_at":"2026-08-10T14:20:41.567147Z","title":"Chen, Zhe Zhao, and Ed Huai hsin Chi","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.15430","last_updated":"2025-01-26T07:18:51Z","snapshot_observed_at":"2026-08-11T01:53:44.443741Z","submitted_at":"2025-01-26T07:18:51Z","title":"Evaluating Simple Debiasing Techniques in RoBERTa-based Hate Speech Detection Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T14:20:41.567147Z"},"links":{"cited_paper":"/paper/1707.00075","citing_paper":"/paper/2501.15430"},"observation_digest":"sha256:403991bad5e13578c1d797b4693cc8925ef2cc4a4a9219dd9c982b24f2681443","observation_id":"4d8433e6-41de-4e87-a59c-192f12e09cbf","resolution":{"observed_at":"2026-08-10T14:20:41.567147Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.00075","last_updated":"2017-07-07T01:31:36Z","snapshot_observed_at":"2026-07-06T05:49:19.425378Z","submitted_at":"2017-07-01T01:09:33Z","title":"Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.00075","snapshot_observed_at":"2026-08-09T20:04:48.427077Z","title":"Data decisions and theoretical implications when adversarially learning fair representations","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00156","last_updated":"2025-03-02T20:53:26Z","snapshot_observed_at":"2026-08-10T20:13:19.177264Z","submitted_at":"2025-01-31T20:47:06Z","title":"ALBAR: Adversarial Learning approach to mitigate Biases in Action Recognition","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-09T20:04:48.427077Z"},"links":{"cited_paper":"/paper/1707.00075","citing_paper":"/paper/2502.00156"},"observation_digest":"sha256:2635e978b61a7d20f21051a9b712ac7d9ef305f09396d24c1017290d26f5fc90","observation_id":"d0487568-4e8e-48d8-a121-dba47db10ea3","resolution":{"observed_at":"2026-08-09T20:04:48.427077Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.00075","last_updated":"2017-07-07T01:31:36Z","snapshot_observed_at":"2026-07-06T05:49:19.425378Z","submitted_at":"2017-07-01T01:09:33Z","title":"Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.00075","snapshot_observed_at":"2026-08-05T14:49:31.508612Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.20881","last_updated":"2025-08-28T15:11:49Z","snapshot_observed_at":"2026-08-09T19:14:23.746651Z","submitted_at":"2025-08-28T15:11:49Z","title":"Understanding and evaluating computer vision models through the lens of counterfactuals","version":1},"reference_index":174,"source":"pdf_text","source_observed_at":"2026-08-05T14:49:31.508612Z"},"links":{"cited_paper":"/paper/1707.00075","citing_paper":"/paper/2508.20881"},"observation_digest":"sha256:fffb85ca08f5a27c521a080070f278c2df4ce1e6f2b5898fc57f4655116aa288","observation_id":"123c7456-73c1-42c6-949f-31f69e5e55f6","resolution":{"observed_at":"2026-08-05T14:49:31.508612Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.00075","last_updated":"2017-07-07T01:31:36Z","snapshot_observed_at":"2026-07-06T05:49:19.425378Z","submitted_at":"2017-07-01T01:09:33Z","title":"Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.00075","snapshot_observed_at":"2026-08-04T23:34:22.839522Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.06535","last_updated":"2025-09-08T10:41:10Z","snapshot_observed_at":"2026-08-09T16:06:26.457762Z","submitted_at":"2025-09-08T10:41:10Z","title":"On the Reproducibility of \"FairCLIP: Harnessing Fairness in Vision-Language Learning''","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-04T23:34:22.839522Z"},"links":{"cited_paper":"/paper/1707.00075","citing_paper":"/paper/2509.06535"},"observation_digest":"sha256:dd8c7802abdcec9027f3e1a445a10f456d3550b5c312705f5cc9d1f063213a35","observation_id":"3865cc76-2607-4386-ba3c-94736cea9c25","resolution":{"observed_at":"2026-08-04T23:34:22.839522Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.00075","last_updated":"2017-07-07T01:31:36Z","snapshot_observed_at":"2026-07-06T05:49:19.425378Z","submitted_at":"2017-07-01T01:09:33Z","title":"Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations","version":2},"cited_work":{"arxiv_id":"1707.00075","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1707.00075","snapshot_observed_at":"2026-07-04T22:03:15.723911Z","title":"arXiv preprint arXiv:1707.00075 , year=","venue":null,"work_id":"0117d9fa-a5a1-4b37-9640-77653156d2b8","year":null},"citing_paper":{"arxiv_id":"2605.08651","last_updated":"2026-05-09T03:46:39Z","snapshot_observed_at":"2026-08-11T02:56:35.978660Z","submitted_at":"2026-05-09T03:46:39Z","title":"Privacy-Aware Video Anomaly Detection through Orthogonal Subspace Projection","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-05-12T02:16:34.307559Z"},"links":{"cited_paper":"/paper/1707.00075","citing_paper":"/paper/2605.08651"},"observation_digest":"sha256:fb5e490dedbb60f2159d9965c4e49398a57a53add1c21f1ff3552dd625afe37c","observation_id":"30c51f2e-b258-467a-a15b-9d454df8f501","resolution":{"observed_at":"2026-07-04T22:03:15.723911Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1707.00075/citation-record","integrity":"/paper/1707.00075/integrity","json":"/paper/1707.00075/citation-record.json","paper":"/paper/1707.00075"},"outbound":[],"paper":{"arxiv_id":"1707.00075","last_updated":"2017-07-07T01:31:36Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T05:49:19.425378Z","submitted_at":"2017-07-01T01:09:33Z","title":"Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1707.00075."}