{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ADB2JTGU2BYT2UDQ5HCA276NPN","short_pith_number":"pith:ADB2JTGU","schema_version":"1.0","canonical_sha256":"00c3a4ccd4d0713d5070e9c40d7fcd7b604bdfc01264820b477e9b8d22960827","source":{"kind":"arxiv","id":"2408.04778","version":1},"attestation_state":"computed","paper":{"title":"Exploring Personality-Driven Personalization in XAI: Enhancing User Trust in Gameplay","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.HC","authors_text":"Shijie Wang, Sophie Yang, Zhaoxin Li","submitted_at":"2024-08-08T22:32:13Z","abstract_excerpt":"Tailoring XAI methods to individual needs is crucial for intuitive Human-AI interactions. While context and task goals are vital, factors like user personality traits could also influence method selection. Our study investigates using personality traits to predict user preferences among decision trees, texts, and factor graphs. We trained a Machine Learning model on responses to the Big Five personality test to predict preferences. Deploying these predicted preferences in a navigation game (n=6), we found users more receptive to personalized XAI recommendations, enhancing trust in the system. "},"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":"2408.04778","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2024-08-08T22:32:13Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"241bd243b2b06be87b92466e48820a6f99038f90fc8efd5d12b1d4099d95875f","abstract_canon_sha256":"6f4708cf23141dfad6d6d095da853dd0896bd0b0dbe4282f6838c5ecf1cca266"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:53:43.954085Z","signature_b64":"x5ffNwNXg3UK3MwHMmUqUkLDI6rRrEjknwN1fjZ2M16SYbPcoSMz4KkgE7OkFVSt02nhtQbP2mJKWdSW8KeFAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"00c3a4ccd4d0713d5070e9c40d7fcd7b604bdfc01264820b477e9b8d22960827","last_reissued_at":"2026-07-05T08:53:43.953676Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:53:43.953676Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploring Personality-Driven Personalization in XAI: Enhancing User Trust in Gameplay","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.HC","authors_text":"Shijie Wang, Sophie Yang, Zhaoxin Li","submitted_at":"2024-08-08T22:32:13Z","abstract_excerpt":"Tailoring XAI methods to individual needs is crucial for intuitive Human-AI interactions. While context and task goals are vital, factors like user personality traits could also influence method selection. Our study investigates using personality traits to predict user preferences among decision trees, texts, and factor graphs. We trained a Machine Learning model on responses to the Big Five personality test to predict preferences. Deploying these predicted preferences in a navigation game (n=6), we found users more receptive to personalized XAI recommendations, enhancing trust in the system. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.04778","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/2408.04778/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":"2408.04778","created_at":"2026-07-05T08:53:43.953733+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.04778v1","created_at":"2026-07-05T08:53:43.953733+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.04778","created_at":"2026-07-05T08:53:43.953733+00:00"},{"alias_kind":"pith_short_12","alias_value":"ADB2JTGU2BYT","created_at":"2026-07-05T08:53:43.953733+00:00"},{"alias_kind":"pith_short_16","alias_value":"ADB2JTGU2BYT2UDQ","created_at":"2026-07-05T08:53:43.953733+00:00"},{"alias_kind":"pith_short_8","alias_value":"ADB2JTGU","created_at":"2026-07-05T08:53:43.953733+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.08374","citing_title":"Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognition","ref_index":3,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ADB2JTGU2BYT2UDQ5HCA276NPN","json":"https://pith.science/pith/ADB2JTGU2BYT2UDQ5HCA276NPN.json","graph_json":"https://pith.science/api/pith-number/ADB2JTGU2BYT2UDQ5HCA276NPN/graph.json","events_json":"https://pith.science/api/pith-number/ADB2JTGU2BYT2UDQ5HCA276NPN/events.json","paper":"https://pith.science/paper/ADB2JTGU"},"agent_actions":{"view_html":"https://pith.science/pith/ADB2JTGU2BYT2UDQ5HCA276NPN","download_json":"https://pith.science/pith/ADB2JTGU2BYT2UDQ5HCA276NPN.json","view_paper":"https://pith.science/paper/ADB2JTGU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.04778&json=true","fetch_graph":"https://pith.science/api/pith-number/ADB2JTGU2BYT2UDQ5HCA276NPN/graph.json","fetch_events":"https://pith.science/api/pith-number/ADB2JTGU2BYT2UDQ5HCA276NPN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ADB2JTGU2BYT2UDQ5HCA276NPN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ADB2JTGU2BYT2UDQ5HCA276NPN/action/storage_attestation","attest_author":"https://pith.science/pith/ADB2JTGU2BYT2UDQ5HCA276NPN/action/author_attestation","sign_citation":"https://pith.science/pith/ADB2JTGU2BYT2UDQ5HCA276NPN/action/citation_signature","submit_replication":"https://pith.science/pith/ADB2JTGU2BYT2UDQ5HCA276NPN/action/replication_record"}},"created_at":"2026-07-05T08:53:43.953733+00:00","updated_at":"2026-07-05T08:53:43.953733+00:00"}