{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:LTEXOPHKH7YMSK3BYGOSEARTDA","short_pith_number":"pith:LTEXOPHK","schema_version":"1.0","canonical_sha256":"5cc9773cea3ff0c92b61c19d220233180f877f50f20305cf0523f73a52dbdc41","source":{"kind":"arxiv","id":"2605.16041","version":1},"attestation_state":"computed","paper":{"title":"Explainable AI Isn't Enough! Rethinking Algorithmic Contestability","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Standard XAI tools like counterfactuals only flag nearby errors and fall short of providing grounds to overturn algorithmic decisions.","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Gunnar K\\\"onig, Kristof Meding, Timo Freiesleben","submitted_at":"2026-05-15T15:14:13Z","abstract_excerpt":"Machine learning systems increasingly make life-changing decisions about individuals, such as loan approvals, hiring, and cheating detection, raising a pressing question: how can individuals respond to negative decisions made by these opaque systems? While explainable artificial intelligence (XAI) has largely focused on algorithmic recourse -- helping individuals change their features to obtain a desired outcome -- the parallel problem of algorithmic contestability -- helping individuals review and correct erroneous algorithmic decisions -- has received far less attention, despite its central "},"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":true,"formal_links_present":true},"canonical_record":{"source":{"id":"2605.16041","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2026-05-15T15:14:13Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a0db3d968f591ba5fa8dcb37f28b0f1e23de34a4610f52f8c71156b5465304bb","abstract_canon_sha256":"f80e64b4fe3565f6722d4fb26e93b29173441afcf4c4f1e85fd7aaabd7a0696c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:01:50.382472Z","signature_b64":"K+e8McJvdIbMOkPgnyLaYSpxIkl/1VfQkkNw+b99AfwhWRfQvhYPZtDvLPP2mk0yzst1t4L1+sOMlKtud82qAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5cc9773cea3ff0c92b61c19d220233180f877f50f20305cf0523f73a52dbdc41","last_reissued_at":"2026-05-20T00:01:50.381946Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:01:50.381946Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Explainable AI Isn't Enough! Rethinking Algorithmic Contestability","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Standard XAI tools like counterfactuals only flag nearby errors and fall short of providing grounds to overturn algorithmic decisions.","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Gunnar K\\\"onig, Kristof Meding, Timo Freiesleben","submitted_at":"2026-05-15T15:14:13Z","abstract_excerpt":"Machine learning systems increasingly make life-changing decisions about individuals, such as loan approvals, hiring, and cheating detection, raising a pressing question: how can individuals respond to negative decisions made by these opaque systems? While explainable artificial intelligence (XAI) has largely focused on algorithmic recourse -- helping individuals change their features to obtain a desired outcome -- the parallel problem of algorithmic contestability -- helping individuals review and correct erroneous algorithmic decisions -- has received far less attention, despite its central "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Standard XAI explanations such as counterfactuals, LIME, or Anchors, even when combined with human intuitions about decision continuity or monotonicity, reveal only errors in the neighborhood of the individual, but provide insufficient grounds for overturning the decision at hand.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the three identified types of evidence (predictive multiplicity, incorrect feature values, and neglected overruling evidence) render decisions normatively indefensible according to the decision maker's own ethical standards, as stated in the section proposing the operational definition of contestability.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"The paper defines algorithmic contestability as identifying evidence to overturn potentially incorrect decisions and identifies three types of such evidence that make decisions normatively indefensible under the decision maker's standards.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Standard XAI tools like counterfactuals only flag nearby errors and fall short of providing grounds to overturn algorithmic decisions.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"583e7865a3474bd3ada546ece5085084c111d72495695649dea59e03864b3b63"},"source":{"id":"2605.16041","kind":"arxiv","version":1},"verdict":{"id":"d46df7ba-e050-4a28-a033-b63a9689a6c8","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T19:04:18.674887Z","strongest_claim":"Standard XAI explanations such as counterfactuals, LIME, or Anchors, even when combined with human intuitions about decision continuity or monotonicity, reveal only errors in the neighborhood of the individual, but provide insufficient grounds for overturning the decision at hand.","one_line_summary":"The paper defines algorithmic contestability as identifying evidence to overturn potentially incorrect decisions and identifies three types of such evidence that make decisions normatively indefensible under the decision maker's standards.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the three identified types of evidence (predictive multiplicity, incorrect feature values, and neglected overruling evidence) render decisions normatively indefensible according to the decision maker's own ethical standards, as stated in the section proposing the operational definition of contestability.","pith_extraction_headline":"Standard XAI tools like counterfactuals only flag nearby errors and fall short of providing grounds to overturn algorithmic decisions."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16041/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T19:31:19.006861Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T19:10:49.282587Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T17:33:41.560553Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T16:41:55.535547Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"2b6730930bd027816c623f9aa3f9e09411c7738be5d8091287e7d0eaf91581d2"},"references":{"count":115,"sample":[{"doi":"","year":2017,"title":"A practical guide, 1st ed., Cham: Springer International Publishing , volume=","work_id":"75f4696e-e0ac-4664-8fba-535ec6cba9f5","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2019,"title":"Joint European conference on machine learning and knowledge discovery in databases , pages=","work_id":"be333296-d805-4674-b80f-9dee9745df1d","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"European Union , year=","work_id":"7b9df22e-fd7f-4d95-bd8d-59d81aa03539","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2014,"title":"Philosophy of Science , volume=","work_id":"37963991-fb6a-4652-a4e5-b662fb54fc51","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"New essays on semantic externalism and self-knowledge , pages=","work_id":"e4ab2099-ddde-4231-b87f-6fdb323c516a","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":115,"snapshot_sha256":"7fd006412a5d71ba9bb4243bd50b57f3116a3c799ca8d8480579faa16e864540","internal_anchors":1},"formal_canon":{"evidence_count":1,"snapshot_sha256":"e81d08d4d6b4251e3892b14ee051a7e7fd586e05bf9489bb897140d2ba4db475"},"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":"2605.16041","created_at":"2026-05-20T00:01:50.382026+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.16041v1","created_at":"2026-05-20T00:01:50.382026+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16041","created_at":"2026-05-20T00:01:50.382026+00:00"},{"alias_kind":"pith_short_12","alias_value":"LTEXOPHKH7YM","created_at":"2026-05-20T00:01:50.382026+00:00"},{"alias_kind":"pith_short_16","alias_value":"LTEXOPHKH7YMSK3B","created_at":"2026-05-20T00:01:50.382026+00:00"},{"alias_kind":"pith_short_8","alias_value":"LTEXOPHK","created_at":"2026-05-20T00:01:50.382026+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":1,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LTEXOPHKH7YMSK3BYGOSEARTDA","json":"https://pith.science/pith/LTEXOPHKH7YMSK3BYGOSEARTDA.json","graph_json":"https://pith.science/api/pith-number/LTEXOPHKH7YMSK3BYGOSEARTDA/graph.json","events_json":"https://pith.science/api/pith-number/LTEXOPHKH7YMSK3BYGOSEARTDA/events.json","paper":"https://pith.science/paper/LTEXOPHK"},"agent_actions":{"view_html":"https://pith.science/pith/LTEXOPHKH7YMSK3BYGOSEARTDA","download_json":"https://pith.science/pith/LTEXOPHKH7YMSK3BYGOSEARTDA.json","view_paper":"https://pith.science/paper/LTEXOPHK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.16041&json=true","fetch_graph":"https://pith.science/api/pith-number/LTEXOPHKH7YMSK3BYGOSEARTDA/graph.json","fetch_events":"https://pith.science/api/pith-number/LTEXOPHKH7YMSK3BYGOSEARTDA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LTEXOPHKH7YMSK3BYGOSEARTDA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LTEXOPHKH7YMSK3BYGOSEARTDA/action/storage_attestation","attest_author":"https://pith.science/pith/LTEXOPHKH7YMSK3BYGOSEARTDA/action/author_attestation","sign_citation":"https://pith.science/pith/LTEXOPHKH7YMSK3BYGOSEARTDA/action/citation_signature","submit_replication":"https://pith.science/pith/LTEXOPHKH7YMSK3BYGOSEARTDA/action/replication_record"}},"created_at":"2026-05-20T00:01:50.382026+00:00","updated_at":"2026-05-20T00:01:50.382026+00:00"}