{"as_of":"2026-08-12T04:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:550f16e13e089664804a61a9a45c7cc912079acae8fc7e7ca9e1b6f778b4b343","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T05:33:11.869382Z","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":"2026-05-21T11:30:02.491090Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2010.07389","last_updated":"2020-10-14T20:21:01Z","snapshot_observed_at":"2026-07-06T10:04:33.354008Z","submitted_at":"2020-10-14T20:21:01Z","title":"Explainability for fair machine learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.07389","snapshot_observed_at":"2026-08-11T05:33:11.869382Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.17523","last_updated":"2025-01-20T05:44:07Z","snapshot_observed_at":"2026-08-11T05:24:36.334757Z","submitted_at":"2024-12-23T12:47:04Z","title":"Constructing Fair Latent Space for Intersection of Fairness and Explainability","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-11T05:33:11.869382Z"},"links":{"cited_paper":"/paper/2010.07389","citing_paper":"/paper/2412.17523"},"observation_digest":"sha256:5fae1b8bfdae8d43bb73eb772c9fdc4d55279068e320bb7950c73eb0bf3c5dc2","observation_id":"6dc0ae68-7bed-409e-8408-5776765e0054","resolution":{"observed_at":"2026-08-11T05:33:11.869382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.07389","last_updated":"2020-10-14T20:21:01Z","snapshot_observed_at":"2026-07-06T10:04:33.354008Z","submitted_at":"2020-10-14T20:21:01Z","title":"Explainability for fair machine learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.07389","snapshot_observed_at":"2026-08-10T15:39:37.394940Z","title":"Explainability for fair machine learning","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2501.13818","last_updated":"2025-07-29T16:04:56Z","snapshot_observed_at":"2026-08-10T23:14:42.290781Z","submitted_at":"2025-01-23T16:39:09Z","title":"Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T15:39:37.394940Z"},"links":{"cited_paper":"/paper/2010.07389","citing_paper":"/paper/2501.13818"},"observation_digest":"sha256:0f7381b4e04735d384a2f6e123f300df117976f6328a3521ba981269c75c48f8","observation_id":"1486d5c1-3060-4dbe-a4e0-24e4b76985f1","resolution":{"observed_at":"2026-08-10T15:39:37.394940Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.07389","last_updated":"2020-10-14T20:21:01Z","snapshot_observed_at":"2026-07-06T10:04:33.354008Z","submitted_at":"2020-10-14T20:21:01Z","title":"Explainability for fair machine learning","version":1},"cited_work":{"arxiv_id":"2010.07389","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2010.07389","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Explainability for fair machine learning","venue":null,"work_id":"cb94ffed-cf6a-42f0-b75d-29436f9d0e83","year":2010},"citing_paper":{"arxiv_id":"2603.13452","last_updated":"2026-05-15T16:50:28Z","snapshot_observed_at":"2026-07-06T22:49:01.769027Z","submitted_at":"2026-03-13T15:42:31Z","title":"MESD: A Risk-Sensitive Metric for Explanation Fairness Across Intersectional Subgroups","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-15T11:53:43.316620Z"},"links":{"cited_paper":"/paper/2010.07389","citing_paper":"/paper/2603.13452"},"observation_digest":"sha256:6d8efe690925281c035373495a309602ce392d176644c88e86ae008cc2ce4844","observation_id":"ed68d7f4-f9a8-4d1b-a3d6-bf69b616d820","resolution":{"observed_at":"2026-05-15T11:55:33.395091Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2010.07389","last_updated":"2020-10-14T20:21:01Z","snapshot_observed_at":"2026-07-06T10:04:33.354008Z","submitted_at":"2020-10-14T20:21:01Z","title":"Explainability for fair machine learning","version":1},"cited_work":{"arxiv_id":"2010.07389","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2010.07389","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Explainability for fair machine learning","venue":null,"work_id":"cb94ffed-cf6a-42f0-b75d-29436f9d0e83","year":2010},"citing_paper":{"arxiv_id":"2603.13452","last_updated":"2026-05-15T16:50:28Z","snapshot_observed_at":"2026-07-06T22:49:01.769027Z","submitted_at":"2026-03-13T15:42:31Z","title":"MESD: A Risk-Sensitive Metric for Explanation Fairness Across Intersectional Subgroups","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-21T11:28:01.158915Z"},"links":{"cited_paper":"/paper/2010.07389","citing_paper":"/paper/2603.13452"},"observation_digest":"sha256:6543915a72af8c42f4f59ef353d84e2056d9964170eece3ce52f9e14fdd07e88","observation_id":"e60ccf11-8543-4adb-8d70-8ff586a488bd","resolution":{"observed_at":"2026-05-21T11:30:02.493370Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2010.07389","last_updated":"2020-10-14T20:21:01Z","snapshot_observed_at":"2026-07-06T10:04:33.354008Z","submitted_at":"2020-10-14T20:21:01Z","title":"Explainability for fair machine learning","version":1},"cited_work":{"arxiv_id":"2010.07389","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2010.07389","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Explainability for fair machine learning","venue":null,"work_id":"cb94ffed-cf6a-42f0-b75d-29436f9d0e83","year":2010},"citing_paper":{"arxiv_id":"2605.09852","last_updated":"2026-05-11T01:09:06Z","snapshot_observed_at":"2026-08-11T06:14:39.602979Z","submitted_at":"2026-05-11T01:09:06Z","title":"Fairness of Explanations in Artificial Intelligence (AI): A Unifying Framework, Axioms, and Future Direction toward Responsible AI","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-12T05:04:40.193388Z"},"links":{"cited_paper":"/paper/2010.07389","citing_paper":"/paper/2605.09852"},"observation_digest":"sha256:c3fe950904b81ed78b3f4ea367f367d9dc4e3c62a810a10f020aabfb3bbbd6ee","observation_id":"1eeea081-8073-4940-b466-17a9a8026dfb","resolution":{"observed_at":"2026-05-12T05:41:25.277849Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2010.07389","last_updated":"2020-10-14T20:21:01Z","snapshot_observed_at":"2026-07-06T10:04:33.354008Z","submitted_at":"2020-10-14T20:21:01Z","title":"Explainability for fair machine learning","version":1},"cited_work":{"arxiv_id":"2010.07389","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2010.07389","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Explainability for fair machine learning","venue":null,"work_id":"cb94ffed-cf6a-42f0-b75d-29436f9d0e83","year":2010},"citing_paper":{"arxiv_id":"2605.12701","last_updated":"2026-05-12T19:54:25Z","snapshot_observed_at":"2026-08-11T02:07:57.678009Z","submitted_at":"2026-05-12T19:54:25Z","title":"Do Fair Models Reason Fairly? Counterfactual Explanation Consistency for Procedural Fairness in Credit Decisions","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-05-14T20:50:02.096278Z"},"links":{"cited_paper":"/paper/2010.07389","citing_paper":"/paper/2605.12701"},"observation_digest":"sha256:96deb4bd57254b36403ed2042a2b43f5f93272ce7c696db16c4cf523103ee99a","observation_id":"7fc23563-0462-46d9-9f4e-edbd963d8c51","resolution":{"observed_at":"2026-05-14T20:52:59.057608Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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/2010.07389/citation-record","integrity":"/paper/2010.07389/integrity","json":"/paper/2010.07389/citation-record.json","paper":"/paper/2010.07389"},"outbound":[],"paper":{"arxiv_id":"2010.07389","last_updated":"2020-10-14T20:21:01Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T10:04:33.354008Z","submitted_at":"2020-10-14T20:21:01Z","title":"Explainability for fair machine learning"},"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 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2010.07389."}