{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:B5YKRA3W2LKUKW34IKLK54O6NF","short_pith_number":"pith:B5YKRA3W","schema_version":"1.0","canonical_sha256":"0f70a88376d2d5455b7c4296aef1de6958afdd098748e286ff423e8a8a6e86a0","source":{"kind":"arxiv","id":"2506.00047","version":1},"attestation_state":"computed","paper":{"title":"Risks of AI-driven product development and strategies for their mitigation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CE"],"primary_cat":"cs.CY","authors_text":"Jan G\\\"opfert, Jann M. Weinand, Jochen Lin{\\ss}en, Noah Pflugradt, Patrick Kuckertz","submitted_at":"2025-05-28T16:52:44Z","abstract_excerpt":"Humanity is progressing towards automated product development, a trend that promises faster creation of better products and thus the acceleration of technological progress. However, increasing reliance on non-human agents for this process introduces many risks. This perspective aims to initiate a discussion on these risks and appropriate mitigation strategies. To this end, we outline a set of principles for safer AI-driven product development which emphasize human oversight, accountability, and explainable design, among others. The risk assessment covers both technical risks which affect produ"},"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":"2506.00047","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CY","submitted_at":"2025-05-28T16:52:44Z","cross_cats_sorted":["cs.AI","cs.CE"],"title_canon_sha256":"b50e3f43b068073c612307805fc12af55159866bc0a2335c5d4e850011046ac8","abstract_canon_sha256":"0f43327f51890c3e462347533121d72cb5f69f49c102a5d0bba2eb5f9aae76c9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:13:28.067418Z","signature_b64":"q0InJa6t+Of21AvExqkJWNJfrLxZ43Wm1FHqinnPD6fTNdraL+UJpRV73m1iR/Fg7OIN1yde4jA+hgYH9sZ/Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0f70a88376d2d5455b7c4296aef1de6958afdd098748e286ff423e8a8a6e86a0","last_reissued_at":"2026-07-05T11:13:28.066916Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:13:28.066916Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Risks of AI-driven product development and strategies for their mitigation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CE"],"primary_cat":"cs.CY","authors_text":"Jan G\\\"opfert, Jann M. Weinand, Jochen Lin{\\ss}en, Noah Pflugradt, Patrick Kuckertz","submitted_at":"2025-05-28T16:52:44Z","abstract_excerpt":"Humanity is progressing towards automated product development, a trend that promises faster creation of better products and thus the acceleration of technological progress. However, increasing reliance on non-human agents for this process introduces many risks. This perspective aims to initiate a discussion on these risks and appropriate mitigation strategies. To this end, we outline a set of principles for safer AI-driven product development which emphasize human oversight, accountability, and explainable design, among others. The risk assessment covers both technical risks which affect produ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.00047","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/2506.00047/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":"2506.00047","created_at":"2026-07-05T11:13:28.066984+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.00047v1","created_at":"2026-07-05T11:13:28.066984+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.00047","created_at":"2026-07-05T11:13:28.066984+00:00"},{"alias_kind":"pith_short_12","alias_value":"B5YKRA3W2LKU","created_at":"2026-07-05T11:13:28.066984+00:00"},{"alias_kind":"pith_short_16","alias_value":"B5YKRA3W2LKUKW34","created_at":"2026-07-05T11:13:28.066984+00:00"},{"alias_kind":"pith_short_8","alias_value":"B5YKRA3W","created_at":"2026-07-05T11:13:28.066984+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.15110","citing_title":"LLMs and Agentic AI in Insurance Decision-Making: Opportunities and Challenges For Africa","ref_index":98,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/B5YKRA3W2LKUKW34IKLK54O6NF","json":"https://pith.science/pith/B5YKRA3W2LKUKW34IKLK54O6NF.json","graph_json":"https://pith.science/api/pith-number/B5YKRA3W2LKUKW34IKLK54O6NF/graph.json","events_json":"https://pith.science/api/pith-number/B5YKRA3W2LKUKW34IKLK54O6NF/events.json","paper":"https://pith.science/paper/B5YKRA3W"},"agent_actions":{"view_html":"https://pith.science/pith/B5YKRA3W2LKUKW34IKLK54O6NF","download_json":"https://pith.science/pith/B5YKRA3W2LKUKW34IKLK54O6NF.json","view_paper":"https://pith.science/paper/B5YKRA3W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.00047&json=true","fetch_graph":"https://pith.science/api/pith-number/B5YKRA3W2LKUKW34IKLK54O6NF/graph.json","fetch_events":"https://pith.science/api/pith-number/B5YKRA3W2LKUKW34IKLK54O6NF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B5YKRA3W2LKUKW34IKLK54O6NF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B5YKRA3W2LKUKW34IKLK54O6NF/action/storage_attestation","attest_author":"https://pith.science/pith/B5YKRA3W2LKUKW34IKLK54O6NF/action/author_attestation","sign_citation":"https://pith.science/pith/B5YKRA3W2LKUKW34IKLK54O6NF/action/citation_signature","submit_replication":"https://pith.science/pith/B5YKRA3W2LKUKW34IKLK54O6NF/action/replication_record"}},"created_at":"2026-07-05T11:13:28.066984+00:00","updated_at":"2026-07-05T11:13:28.066984+00:00"}