{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:KIUKMGVRF77OETXS7IAEZNRFGU","short_pith_number":"pith:KIUKMGVR","canonical_record":{"source":{"id":"2205.02052","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-04T13:25:21Z","cross_cats_sorted":[],"title_canon_sha256":"e7d0f9c62d8776d2a0bfcc7c9c0748cd8fc64b93b399be4fa52a3cf3554fd613","abstract_canon_sha256":"ccaabf0cfa7a73ee508b97293d1ef4b2697d08401c26f43c255d5c30e3b8e521"},"schema_version":"1.0"},"canonical_sha256":"5228a61ab12ffee24ef2fa004cb62535256a899a2e43c75a7027d600a3a65e05","source":{"kind":"arxiv","id":"2205.02052","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.02052","created_at":"2026-07-05T04:20:24Z"},{"alias_kind":"arxiv_version","alias_value":"2205.02052v1","created_at":"2026-07-05T04:20:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.02052","created_at":"2026-07-05T04:20:24Z"},{"alias_kind":"pith_short_12","alias_value":"KIUKMGVRF77O","created_at":"2026-07-05T04:20:24Z"},{"alias_kind":"pith_short_16","alias_value":"KIUKMGVRF77OETXS","created_at":"2026-07-05T04:20:24Z"},{"alias_kind":"pith_short_8","alias_value":"KIUKMGVR","created_at":"2026-07-05T04:20:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:KIUKMGVRF77OETXS7IAEZNRFGU","target":"record","payload":{"canonical_record":{"source":{"id":"2205.02052","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-04T13:25:21Z","cross_cats_sorted":[],"title_canon_sha256":"e7d0f9c62d8776d2a0bfcc7c9c0748cd8fc64b93b399be4fa52a3cf3554fd613","abstract_canon_sha256":"ccaabf0cfa7a73ee508b97293d1ef4b2697d08401c26f43c255d5c30e3b8e521"},"schema_version":"1.0"},"canonical_sha256":"5228a61ab12ffee24ef2fa004cb62535256a899a2e43c75a7027d600a3a65e05","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:20:24.783014Z","signature_b64":"kPif3kyWpdo/7bkiZpbwP+Vri8A1yHAp5mDNwxeqDsTjOO1NkDsCbioiHJ26OVhVb12xVT9mrpkDgCBjpU7OBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5228a61ab12ffee24ef2fa004cb62535256a899a2e43c75a7027d600a3a65e05","last_reissued_at":"2026-07-05T04:20:24.782573Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:20:24.782573Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.02052","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":"2026-07-05T04:20:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HGraFw+o6xOm9rkcXqfOObOzK2Wm7EmAyizrtEgNOsXK7uFWAcKx+09Yih/4pmos9HTaZ/06C4iRzWEAyDysBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T13:18:18.946485Z"},"content_sha256":"567369ecaa40e13f6fb627aa8a541c5358ffa2c55ae216cafce3ced08d4557c4","schema_version":"1.0","event_id":"sha256:567369ecaa40e13f6fb627aa8a541c5358ffa2c55ae216cafce3ced08d4557c4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:KIUKMGVRF77OETXS7IAEZNRFGU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Exploring Rawlsian Fairness for K-Means Clustering","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Deepak P, Muiris MacCarthaigh, Stanley Simoes","submitted_at":"2022-05-04T13:25:21Z","abstract_excerpt":"We conduct an exploratory study that looks at incorporating John Rawls' ideas on fairness into existing unsupervised machine learning algorithms. Our focus is on the task of clustering, specifically the k-means clustering algorithm. To the best of our knowledge, this is the first work that uses Rawlsian ideas in clustering. Towards this, we attempt to develop a postprocessing technique i.e., one that operates on the cluster assignment generated by the standard k-means clustering algorithm. Our technique perturbs this assignment over a number of iterations to make it fairer according to Rawls' "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.02052","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/2205.02052/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":"2026-07-05T04:20:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fcBS92okJJj02zrOvYgCJquFfmnzcNaV4+fW8NKMdZaPCgYG1Tpebv6VKgTUNLmdd0jeVifjiabG2CE6jByKAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T13:18:18.947030Z"},"content_sha256":"7b234e224958409f2ff7f7e4e229719258006dd62caaa6cd29df3510853e070e","schema_version":"1.0","event_id":"sha256:7b234e224958409f2ff7f7e4e229719258006dd62caaa6cd29df3510853e070e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KIUKMGVRF77OETXS7IAEZNRFGU/bundle.json","state_url":"https://pith.science/pith/KIUKMGVRF77OETXS7IAEZNRFGU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KIUKMGVRF77OETXS7IAEZNRFGU/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":"2026-08-04T13:18:18Z","links":{"resolver":"https://pith.science/pith/KIUKMGVRF77OETXS7IAEZNRFGU","bundle":"https://pith.science/pith/KIUKMGVRF77OETXS7IAEZNRFGU/bundle.json","state":"https://pith.science/pith/KIUKMGVRF77OETXS7IAEZNRFGU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KIUKMGVRF77OETXS7IAEZNRFGU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:KIUKMGVRF77OETXS7IAEZNRFGU","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":"ccaabf0cfa7a73ee508b97293d1ef4b2697d08401c26f43c255d5c30e3b8e521","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-04T13:25:21Z","title_canon_sha256":"e7d0f9c62d8776d2a0bfcc7c9c0748cd8fc64b93b399be4fa52a3cf3554fd613"},"schema_version":"1.0","source":{"id":"2205.02052","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.02052","created_at":"2026-07-05T04:20:24Z"},{"alias_kind":"arxiv_version","alias_value":"2205.02052v1","created_at":"2026-07-05T04:20:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.02052","created_at":"2026-07-05T04:20:24Z"},{"alias_kind":"pith_short_12","alias_value":"KIUKMGVRF77O","created_at":"2026-07-05T04:20:24Z"},{"alias_kind":"pith_short_16","alias_value":"KIUKMGVRF77OETXS","created_at":"2026-07-05T04:20:24Z"},{"alias_kind":"pith_short_8","alias_value":"KIUKMGVR","created_at":"2026-07-05T04:20:24Z"}],"graph_snapshots":[{"event_id":"sha256:7b234e224958409f2ff7f7e4e229719258006dd62caaa6cd29df3510853e070e","target":"graph","created_at":"2026-07-05T04:20:24Z","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/2205.02052/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We conduct an exploratory study that looks at incorporating John Rawls' ideas on fairness into existing unsupervised machine learning algorithms. Our focus is on the task of clustering, specifically the k-means clustering algorithm. To the best of our knowledge, this is the first work that uses Rawlsian ideas in clustering. Towards this, we attempt to develop a postprocessing technique i.e., one that operates on the cluster assignment generated by the standard k-means clustering algorithm. Our technique perturbs this assignment over a number of iterations to make it fairer according to Rawls' ","authors_text":"Deepak P, Muiris MacCarthaigh, Stanley Simoes","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-04T13:25:21Z","title":"Exploring Rawlsian Fairness for K-Means Clustering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.02052","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":"sha256:567369ecaa40e13f6fb627aa8a541c5358ffa2c55ae216cafce3ced08d4557c4","target":"record","created_at":"2026-07-05T04:20:24Z","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":"ccaabf0cfa7a73ee508b97293d1ef4b2697d08401c26f43c255d5c30e3b8e521","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-04T13:25:21Z","title_canon_sha256":"e7d0f9c62d8776d2a0bfcc7c9c0748cd8fc64b93b399be4fa52a3cf3554fd613"},"schema_version":"1.0","source":{"id":"2205.02052","kind":"arxiv","version":1}},"canonical_sha256":"5228a61ab12ffee24ef2fa004cb62535256a899a2e43c75a7027d600a3a65e05","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5228a61ab12ffee24ef2fa004cb62535256a899a2e43c75a7027d600a3a65e05","first_computed_at":"2026-07-05T04:20:24.782573Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:20:24.782573Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kPif3kyWpdo/7bkiZpbwP+Vri8A1yHAp5mDNwxeqDsTjOO1NkDsCbioiHJ26OVhVb12xVT9mrpkDgCBjpU7OBw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:20:24.783014Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.02052","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:567369ecaa40e13f6fb627aa8a541c5358ffa2c55ae216cafce3ced08d4557c4","sha256:7b234e224958409f2ff7f7e4e229719258006dd62caaa6cd29df3510853e070e"],"state_sha256":"0998077db000c4825ece8703f6d9b34bf10e85dbeaebc2ab68ca6859fc13609c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8h9NoNx/6oMedTXjFRIZoRCzS3rnpM32lCx/M0JhWIDLnMkzm/eze7FMvNyjGM7hr87MgKwpGfkFaFP2iJmYAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T13:18:18.952703Z","bundle_sha256":"d4b19095a02ac76ccc6efbff009a8d58c0d08ab9bcc06887baa846285fef897f"}}