{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:26XNQRIWGMBWKTQ77PNLBR53I4","short_pith_number":"pith:26XNQRIW","canonical_record":{"source":{"id":"2208.05126","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-08-10T03:41:48Z","cross_cats_sorted":["cs.CY","cs.HC","stat.ME"],"title_canon_sha256":"ad28427d00c86926beeaf9dc68dea781fdd42dbc149c6223f8e51896672a94a6","abstract_canon_sha256":"67793b0cd63300a30cdf155e6b52da408454c38ec05346c858b620c5d0348f21"},"schema_version":"1.0"},"canonical_sha256":"d7aed845163303654e1ffbdab0c7bb472b0cba134f24ba8eaf2e0667e7a51d05","source":{"kind":"arxiv","id":"2208.05126","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.05126","created_at":"2026-07-05T04:47:32Z"},{"alias_kind":"arxiv_version","alias_value":"2208.05126v1","created_at":"2026-07-05T04:47:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.05126","created_at":"2026-07-05T04:47:32Z"},{"alias_kind":"pith_short_12","alias_value":"26XNQRIWGMBW","created_at":"2026-07-05T04:47:32Z"},{"alias_kind":"pith_short_16","alias_value":"26XNQRIWGMBWKTQ7","created_at":"2026-07-05T04:47:32Z"},{"alias_kind":"pith_short_8","alias_value":"26XNQRIW","created_at":"2026-07-05T04:47:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:26XNQRIWGMBWKTQ77PNLBR53I4","target":"record","payload":{"canonical_record":{"source":{"id":"2208.05126","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-08-10T03:41:48Z","cross_cats_sorted":["cs.CY","cs.HC","stat.ME"],"title_canon_sha256":"ad28427d00c86926beeaf9dc68dea781fdd42dbc149c6223f8e51896672a94a6","abstract_canon_sha256":"67793b0cd63300a30cdf155e6b52da408454c38ec05346c858b620c5d0348f21"},"schema_version":"1.0"},"canonical_sha256":"d7aed845163303654e1ffbdab0c7bb472b0cba134f24ba8eaf2e0667e7a51d05","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:47:32.606672Z","signature_b64":"MZPiXyzRtDZ5nUIm9WYQSlHhXMf99sIWExDLVu/oQU0n8A7RV0ipt81YkZvZHf1ezvnuSvTrcgRMw57hqEZrBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d7aed845163303654e1ffbdab0c7bb472b0cba134f24ba8eaf2e0667e7a51d05","last_reissued_at":"2026-07-05T04:47:32.606162Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:47:32.606162Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2208.05126","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:47:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nGkPJ3kq+wUQrqtApVYIDQd4RtIG1zgeB3Pd50k8aEx6ZXjK3gSSicdwKgx08LNhDfE29wgu3S1cnzt/8TP8Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T18:38:36.841569Z"},"content_sha256":"c76c39f92c28e6c88478cf2d6902923dbe3944b8ffc8c083bc538a23b9dc9e2b","schema_version":"1.0","event_id":"sha256:c76c39f92c28e6c88478cf2d6902923dbe3944b8ffc8c083bc538a23b9dc9e2b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:26XNQRIWGMBWKTQ77PNLBR53I4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY","cs.HC","stat.ME"],"primary_cat":"cs.LG","authors_text":"Bhavya Ghai, Klaus Mueller","submitted_at":"2022-08-10T03:41:48Z","abstract_excerpt":"With the rise of AI, algorithms have become better at learning underlying patterns from the training data including ingrained social biases based on gender, race, etc. Deployment of such algorithms to domains such as hiring, healthcare, law enforcement, etc. has raised serious concerns about fairness, accountability, trust and interpretability in machine learning algorithms. To alleviate this problem, we propose D-BIAS, a visual interactive tool that embodies human-in-the-loop AI approach for auditing and mitigating social biases from tabular datasets. It uses a graphical causal model to repre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.05126","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2208.05126/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:47:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R0Dhiwvw4jFFXHfr7Kx69H4WzhtkyzGvZfkezk3xjbyEj6ztO5HGpBon88LisyLn3XjWJRongJSIFBdCy3qtCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T18:38:36.842091Z"},"content_sha256":"1dff6a68f150c1d18079ef79e7d49077b4aea023a0641a7715a148bf918b7f9a","schema_version":"1.0","event_id":"sha256:1dff6a68f150c1d18079ef79e7d49077b4aea023a0641a7715a148bf918b7f9a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/26XNQRIWGMBWKTQ77PNLBR53I4/bundle.json","state_url":"https://pith.science/pith/26XNQRIWGMBWKTQ77PNLBR53I4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/26XNQRIWGMBWKTQ77PNLBR53I4/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T18:38:36Z","links":{"resolver":"https://pith.science/pith/26XNQRIWGMBWKTQ77PNLBR53I4","bundle":"https://pith.science/pith/26XNQRIWGMBWKTQ77PNLBR53I4/bundle.json","state":"https://pith.science/pith/26XNQRIWGMBWKTQ77PNLBR53I4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/26XNQRIWGMBWKTQ77PNLBR53I4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:26XNQRIWGMBWKTQ77PNLBR53I4","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"67793b0cd63300a30cdf155e6b52da408454c38ec05346c858b620c5d0348f21","cross_cats_sorted":["cs.CY","cs.HC","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-08-10T03:41:48Z","title_canon_sha256":"ad28427d00c86926beeaf9dc68dea781fdd42dbc149c6223f8e51896672a94a6"},"schema_version":"1.0","source":{"id":"2208.05126","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.05126","created_at":"2026-07-05T04:47:32Z"},{"alias_kind":"arxiv_version","alias_value":"2208.05126v1","created_at":"2026-07-05T04:47:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.05126","created_at":"2026-07-05T04:47:32Z"},{"alias_kind":"pith_short_12","alias_value":"26XNQRIWGMBW","created_at":"2026-07-05T04:47:32Z"},{"alias_kind":"pith_short_16","alias_value":"26XNQRIWGMBWKTQ7","created_at":"2026-07-05T04:47:32Z"},{"alias_kind":"pith_short_8","alias_value":"26XNQRIW","created_at":"2026-07-05T04:47:32Z"}],"graph_snapshots":[{"event_id":"sha256:1dff6a68f150c1d18079ef79e7d49077b4aea023a0641a7715a148bf918b7f9a","target":"graph","created_at":"2026-07-05T04:47:32Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2208.05126/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With the rise of AI, algorithms have become better at learning underlying patterns from the training data including ingrained social biases based on gender, race, etc. Deployment of such algorithms to domains such as hiring, healthcare, law enforcement, etc. has raised serious concerns about fairness, accountability, trust and interpretability in machine learning algorithms. To alleviate this problem, we propose D-BIAS, a visual interactive tool that embodies human-in-the-loop AI approach for auditing and mitigating social biases from tabular datasets. It uses a graphical causal model to repre","authors_text":"Bhavya Ghai, Klaus Mueller","cross_cats":["cs.CY","cs.HC","stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-08-10T03:41:48Z","title":"D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.05126","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c76c39f92c28e6c88478cf2d6902923dbe3944b8ffc8c083bc538a23b9dc9e2b","target":"record","created_at":"2026-07-05T04:47:32Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"67793b0cd63300a30cdf155e6b52da408454c38ec05346c858b620c5d0348f21","cross_cats_sorted":["cs.CY","cs.HC","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-08-10T03:41:48Z","title_canon_sha256":"ad28427d00c86926beeaf9dc68dea781fdd42dbc149c6223f8e51896672a94a6"},"schema_version":"1.0","source":{"id":"2208.05126","kind":"arxiv","version":1}},"canonical_sha256":"d7aed845163303654e1ffbdab0c7bb472b0cba134f24ba8eaf2e0667e7a51d05","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d7aed845163303654e1ffbdab0c7bb472b0cba134f24ba8eaf2e0667e7a51d05","first_computed_at":"2026-07-05T04:47:32.606162Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:47:32.606162Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MZPiXyzRtDZ5nUIm9WYQSlHhXMf99sIWExDLVu/oQU0n8A7RV0ipt81YkZvZHf1ezvnuSvTrcgRMw57hqEZrBg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:47:32.606672Z","signed_message":"canonical_sha256_bytes"},"source_id":"2208.05126","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c76c39f92c28e6c88478cf2d6902923dbe3944b8ffc8c083bc538a23b9dc9e2b","sha256:1dff6a68f150c1d18079ef79e7d49077b4aea023a0641a7715a148bf918b7f9a"],"state_sha256":"aca7a8872f9b466d8f6a3f214b94507be86bbb3b1c61c18227e18508fd363bbc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RhZOvtJZ6ld1DCCOs5DomVQ1fG48BovO/5fDMnWYE7btFOSCfx1aCLm6FwCYODp78Ad9xT+QZcj7igqcn2aHDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T18:38:36.846715Z","bundle_sha256":"344f068e2d6052e3adb3d6812ed42b7b0551455890aced0ede366719ebd8ad14"}}