{"total":11,"items":[{"citing_arxiv_id":"2606.22142","ref_index":53,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"RoboLineage: Agent-Native Data Lifecycle Governance Across Robot Policy Iterations","primary_cat":"cs.RO","submitted_at":"2026-06-20T16:48:51+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"RoboLineage introduces an agent-native data lifecycle governance system that represents robot policy iteration steps as typed lineage artifacts to improve speed and auditability in real-robot workflows.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.09408","ref_index":63,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Can Data Work be Reparative?","primary_cat":"cs.CY","submitted_at":"2026-06-08T12:25:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Ethnographic study of feminist civic-tech data work argues reparative AI dataset production requires resetting accountability ties to center those harmed by current practices.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.07865","ref_index":10,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Instrumented data for causal scientific machine learning","primary_cat":"cs.LG","submitted_at":"2026-06-05T21:53:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Instrumented data augments observations with mechanistic models, uncertainty, and counterfactuals to enable causal interventions via Pearl's do-operator in scientific machine learning.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.18302","ref_index":16,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"What Would GPT Click: Practical Effects of Human-AI Behavioral Misalignment and the Cost of Synthetic Participants in User Experience","primary_cat":"cs.HC","submitted_at":"2026-05-18T12:20:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"GPT produces click distributions significantly different from real humans in 53% of UX first-click tasks, with prompting techniques like personas and chain-of-thought failing to improve alignment.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.11345","ref_index":130,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Evaluating Structured Documentation as a Tool for Reflexivity in Dataset Development","primary_cat":"cs.CY","submitted_at":"2026-05-11T23:55:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Structured dataset documentation shows little engagement with major reflexivity themes from FAccT literature, leading to a new codebook and extended datasheet questions.","context_count":1,"top_context_role":"dataset","top_context_polarity":"background","context_text":"[128] Morgan Klaus Scheuerman, Alex Hanna, and Emily Denton. 2021. Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development.Proceedings of the ACM on Human-Computer Interaction5, CSCW2 (2021), 1-37. doi:10.1145/3476058 [129] Benjamin M Schmidt. 2012. Words alone: Dismantling topic models in the humanities.Journal of Digital Humanities2, 1 (2012), 49-65. [130] Patrick Schramowski, Christopher Tauchmann, and Kristian Kersting. 2022. Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content?. InProceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22). Association for Computing Machinery, New York, NY, USA, 1350-1361. doi:10."},{"citing_arxiv_id":"2605.07389","ref_index":25,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Exploring CoCo Challenges in ML Engineering Teams: Insights From the Semiconductor Industry","primary_cat":"cs.SE","submitted_at":"2026-05-08T07:43:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Interviews in a semiconductor company reveal 16 collaboration and communication challenges in ML engineering teams, with unclear roles and responsibilities as the top issue, and list effective mitigation practices under hardware-driven constraints.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.05908","ref_index":3,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Architecture-agnostic Lipschitz-constant Bayesian header and its application to resolve semantically proximal classification errors with vision transformers","primary_cat":"cs.CV","submitted_at":"2026-05-07T09:18:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"LipB-ViT adds bi-Lipschitz Bayesian layers to vision transformers and uses uncertainty-aware fusion to identify corrupted labels with over 93% recall at 15% noise, beating kNN baselines.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.06000","ref_index":50,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Regimes of Scale in AI Meteorology","primary_cat":"cs.HC","submitted_at":"2026-04-07T15:28:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"AI/ML weather tools face integration challenges from mismatched 'regimes of scale' in how data and models are organized compared to traditional meteorology practices.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"For example, Hohman et. al theorize \"data iteration\" in AI/ML by pulling from interviewees across \"diverse ML domains\" including NLP, computer vision, and \"Applied ML + Systems\" [23]. Likewise, Sambasivan et. al's study of \"high-stakes domains\" merges healthcare, food/agriculture, environment/climate, and more to study \"data cascades\" via interviews of AI practitioners [50]. Even within specific domains like healthcare, \"domain application\" can be used to work across different subdomains: for example, in their study of public health datafication, Thakkar et. al use \"application domain\" to split public health into project domains including \"Maternal Health, \" \"Sexual Health, \" and \"Other\" [61]. The split across \"application domains\" generally originates in domain-agnostic interviewees"},{"citing_arxiv_id":"2604.02641","ref_index":47,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"The Paradox of Prioritization in Public Sector Algorithms","primary_cat":"cs.HC","submitted_at":"2026-04-03T02:10:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Prioritization algorithms in public services generate relative disparities among intersectional groups as resources become scarce, intensifying perceptions of inequality.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Furthermore, we focused on one round of resource allocation through prioritization and did not consider how disparities can emerge under other distribution policies, such as first-come-first-serve or random lotteries. In practice, public-sector prioritization algorithms operate in different policy and organizational contexts, where multiple distributive policies and algorithms operate simultaneously [47, 55]. These factors may amplify or diminish the disparities we identify in our paper. For example, Saxena and Guha [49] show that once an allegation of child abuse is made and a family engages with the child welfare system, multiple prioritization algorithms may be deployed at multiple stages of a family's journey in the system. Thus, the extent to which the disparities we observe in our work manifest in practice will depend on specific"},{"citing_arxiv_id":"2602.11318","ref_index":273,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"The Consensus Trap: Dissecting Subjectivity and the \"Ground Truth\" Illusion in Data Annotation","primary_cat":"cs.AI","submitted_at":"2026-02-11T19:45:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A literature review concludes that pursuing consensus in data annotation creates biased AI by dismissing subjective disagreements and enforcing geographic hegemony, and proposes mapping diversity instead.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems(2020). doi:10.1145/3313831. 3376506 [272] Mike Schaekermann, Carrie J. Cai, Abigail E. Huang, and Rory Sayres. 2020. Expert Discussions Improve Comprehension of Difficult Cases in Medical Image Assessment.Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems(2020). doi:10. 1145/3313831.3376290 [273] Ulrike Schäfer, Lars Sipos, and Claudia Müller-Birn. 2025. 'The AI is uncertain, so am I. What now?': Navigating Shortcomings of Uncertainty Representations in Human-AI Collaboration with Capability-focused Guidance.Proc. ACM Hum.-Comput. Interact.(2025). doi:10.1145/3757451 [274] Morgan Klaus Scheuerman and Jed R. Brubaker. 2024. Products of Positionality: How Tech Workers Shape Identity Concepts in"},{"citing_arxiv_id":"2211.05100","ref_index":128,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"BLOOM: A 176B-Parameter Open-Access Multilingual Language Model","primary_cat":"cs.CL","submitted_at":"2022-11-09T18:48:09+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"BLOOM is a 176B-parameter open-access multilingual language model trained on the ROOTS corpus that achieves competitive performance on benchmarks, with improved results after multitask prompted finetuning.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}