{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:6WIMAZPCKTBKPEGFWIFMYBGV7E","short_pith_number":"pith:6WIMAZPC","schema_version":"1.0","canonical_sha256":"f590c065e254c2a790c5b20acc04d5f92e262f0edddbdb429897b1acdcf3cf06","source":{"kind":"arxiv","id":"2110.03524","version":1},"attestation_state":"computed","paper":{"title":"Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"John Dickerson, Naveen Raman, Sanket Shah","submitted_at":"2021-10-07T14:53:37Z","abstract_excerpt":"Rideshare and ride-pooling platforms use artificial intelligence-based matching algorithms to pair riders and drivers. However, these platforms can induce inequality either through an unequal income distribution or disparate treatment of riders. We investigate two methods to reduce forms of inequality in ride-pooling platforms: (1) incorporating fairness constraints into the objective function and (2) redistributing income to drivers to reduce income fluctuation and inequality. To evaluate our solutions, we use the New York City taxi data set. For the first method, we find that optimizing for "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2110.03524","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2021-10-07T14:53:37Z","cross_cats_sorted":[],"title_canon_sha256":"754bd94acf54c432e9ab5eec1c5c1092824cba7dc3afea01e668dcc384405b3f","abstract_canon_sha256":"3836dd8798dd941856b6bb2eb0215d74d1fb8d425bf02b19e31b8b3a702d8d6c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:20:43.531949Z","signature_b64":"ZO2yq0jQRBFDWkLfIT7i6fvhnhAFr6Q/UbOFnqiUmNRhUfnbC2pQOjFJ4XwmV6AZC4NVe2axvu7wXRE5O3vlAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f590c065e254c2a790c5b20acc04d5f92e262f0edddbdb429897b1acdcf3cf06","last_reissued_at":"2026-07-05T03:20:43.531579Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:20:43.531579Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"John Dickerson, Naveen Raman, Sanket Shah","submitted_at":"2021-10-07T14:53:37Z","abstract_excerpt":"Rideshare and ride-pooling platforms use artificial intelligence-based matching algorithms to pair riders and drivers. However, these platforms can induce inequality either through an unequal income distribution or disparate treatment of riders. We investigate two methods to reduce forms of inequality in ride-pooling platforms: (1) incorporating fairness constraints into the objective function and (2) redistributing income to drivers to reduce income fluctuation and inequality. To evaluate our solutions, we use the New York City taxi data set. For the first method, we find that optimizing for "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.03524","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/2110.03524/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2110.03524","created_at":"2026-07-05T03:20:43.531628+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.03524v1","created_at":"2026-07-05T03:20:43.531628+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.03524","created_at":"2026-07-05T03:20:43.531628+00:00"},{"alias_kind":"pith_short_12","alias_value":"6WIMAZPCKTBK","created_at":"2026-07-05T03:20:43.531628+00:00"},{"alias_kind":"pith_short_16","alias_value":"6WIMAZPCKTBKPEGF","created_at":"2026-07-05T03:20:43.531628+00:00"},{"alias_kind":"pith_short_8","alias_value":"6WIMAZPC","created_at":"2026-07-05T03:20:43.531628+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.04167","citing_title":"Smart Transportation Without Neurons -- Fair Metro Network Expansion with Tabular Reinforcement Learning","ref_index":11,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6WIMAZPCKTBKPEGFWIFMYBGV7E","json":"https://pith.science/pith/6WIMAZPCKTBKPEGFWIFMYBGV7E.json","graph_json":"https://pith.science/api/pith-number/6WIMAZPCKTBKPEGFWIFMYBGV7E/graph.json","events_json":"https://pith.science/api/pith-number/6WIMAZPCKTBKPEGFWIFMYBGV7E/events.json","paper":"https://pith.science/paper/6WIMAZPC"},"agent_actions":{"view_html":"https://pith.science/pith/6WIMAZPCKTBKPEGFWIFMYBGV7E","download_json":"https://pith.science/pith/6WIMAZPCKTBKPEGFWIFMYBGV7E.json","view_paper":"https://pith.science/paper/6WIMAZPC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.03524&json=true","fetch_graph":"https://pith.science/api/pith-number/6WIMAZPCKTBKPEGFWIFMYBGV7E/graph.json","fetch_events":"https://pith.science/api/pith-number/6WIMAZPCKTBKPEGFWIFMYBGV7E/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6WIMAZPCKTBKPEGFWIFMYBGV7E/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6WIMAZPCKTBKPEGFWIFMYBGV7E/action/storage_attestation","attest_author":"https://pith.science/pith/6WIMAZPCKTBKPEGFWIFMYBGV7E/action/author_attestation","sign_citation":"https://pith.science/pith/6WIMAZPCKTBKPEGFWIFMYBGV7E/action/citation_signature","submit_replication":"https://pith.science/pith/6WIMAZPCKTBKPEGFWIFMYBGV7E/action/replication_record"}},"created_at":"2026-07-05T03:20:43.531628+00:00","updated_at":"2026-07-05T03:20:43.531628+00:00"}