{"total":13,"items":[{"citing_arxiv_id":"2606.20258","ref_index":27,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Editorial Alignment: A Participatory Approach to Engaging Editorial Expertise in LLM-mediated Knowledge Dissemination","primary_cat":"cs.HC","submitted_at":"2026-06-18T14:07:47+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"The paper introduces 'editorial alignment' as a participatory design practice that treats editorial standards as design artifacts to guide LLM behavior in knowledge dissemination, shown through workshops at one Nordic institution.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.12923","ref_index":7,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Order Is Not Control","primary_cat":"cs.LG","submitted_at":"2026-06-11T05:27:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Order is distinct from control, where control is defined as a local receiver-gated response law demonstrated across biological circuits and LLM response panels with reported prediction accuracies of 72-84%.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.06674","ref_index":34,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"What Do People Actually Want From AI? Mapping Preference Plurality","primary_cat":"cs.CL","submitted_at":"2026-06-04T19:47:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Open-ended preference data reveals substantial plurality in what people want from AI and divergent interpretations of shared values such as truthfulness.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.17697","ref_index":90,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Scrutinizing Index-Based Risk Assessments: A Case Study in NYC Decision-making for Heat Emergency Management","primary_cat":"cs.CY","submitted_at":"2026-05-17T23:36:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Sensitivity analyses of NYC heat emergency indices show that reasonable variations in input variables and spatial scale lead to substantially different risk scores affecting downstream government decisions.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"However, they can be sensitive to the choice of inputs and methods and require strong assumptions. Though simple and transparent, they can still lead to inconsistent and arbitrary outcomes [66]. 2.2 Connections to Algorithmic Fairness Literature There are several connections with ongoing work in the algorithmic fairness literature. We draw on prior work on geographic targeting for eviction outreach [90, 91], environmental compliance [17, 66, 74], and opioid prevention [9, 10, 64]. In particular, Benami et al. [17] highlight how algorithm design can help clarify some of the implicit assumptions in high-level policy decisions. These papers also raise concerns related to spatial inequality, stakeholder capacity, and the potential to exacerbate rather than mitigate disparities."},{"citing_arxiv_id":"2605.17510","ref_index":35,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Scale-Dependent Collective Adaptation in Self-Amending LLM Societies: A Cross-Family Study of Emergent Governance","primary_cat":"nlin.AO","submitted_at":"2026-05-17T15:45:47+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"LLM societies in Nomic show non-monotonic collective adaptation peaking at mid-scales, with smaller models rule-inert and larger ones restrictive.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.12434","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Pluralistic-Alignment Urbanism: Operationalizing a Right to AI for Inclusive Public Space","primary_cat":"cs.CY","submitted_at":"2026-05-15T23:59:37+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Introduces PAU as a governance architecture for municipal AI in public spaces, informed by case studies on subgroup-aware scaling (R2=0.89) and pluralistic preference data that treats neutrality as indeterminacy.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16291","ref_index":48,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"AI of the People, by the People, for the People: A Social Choice Approach to Collective Control of Artificial Intelligence","primary_cat":"cs.CY","submitted_at":"2026-04-14T07:42:19+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Proposes applying social choice theory as a modeling language and axiomatic tool for incorporating collective input across the ML development pipeline.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.06600","ref_index":39,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"IntervenSim: Intervention-Aware Social Network Simulation for Opinion Dynamics","primary_cat":"cs.SI","submitted_at":"2026-04-08T02:34:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"IntervenSim is an intervention-aware social network simulation that couples source interventions with crowd interactions in a feedback loop, improving MAPE by 41.6% and DTW by 66.9% over prior static frameworks on real-world events.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"[37] Mei Li, Xiang Wang, Kai Gao, and Shanshan Zhang. 2017. A survey on information diffusion in online social networks: Models and methods.Information8, 4 (2017), 118. [38] Wenbing Li, Zikai Song, Hang Zhou, Yunyao Zhang, Junqing Yu, and Wei Yang. 2025. LoRA-Mixer: Coordinate Modular LoRA Experts Through Serial Attention Routing.arXiv preprint arXiv:2507.00029(2025). [39] Wenbing Li, Hang Zhou, Junqing Yu, Zikai Song, and Wei Yang. 2024. Coupled mamba: Enhanced multimodal fusion with coupled state space model.Advances in Neural Information Processing Systems37 (2024), 59808-59832. [40] Junjie Liao, Huacong Tang, Zhou Ziheng, Yizhou Wang, and Fangwei Zhong. 2026. How do Role Models Shape Collective Morality? Exemplar-Driven Moral"},{"citing_arxiv_id":"2604.02720","ref_index":52,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Cognitive Comparability and the Limits of Governance: Evaluating Authority Under Radical Capability Asymmetry","primary_cat":"cs.CY","submitted_at":"2026-04-03T04:26:18+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Superintelligence strategy: Expert version. arXiv:2503.05628, 2025. [50] Liesbet Hooghe and Gary Marks.Multi-Level Governance and European Integration. Rowman & Littlefield, Lanham, MD, 2001. [51] Liesbet Hooghe and Gary Marks. Unraveling the central state, but how? Types of multi- level governance.American Political Science Review, 97(2):233-243, 2003. doi: 10.1017/ S0003055403000649. [52] Saffron Huang, Divya Siddarth, Liane Lovitt, Thomas I. Liao, Esin Durmus, Alex Tamkin, and Deep Ganguli. Collective constitutional AI: Aligning a language model with public input. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024. doi: 10.1145/3630106.3658979. [53] Evan Hubinger, Chris van Merwijk, Vladimir Mikulik, Joar Skalse, and Scott Garrabrant."},{"citing_arxiv_id":"2602.15338","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Discovering Implicit Large Language Model Alignment Objectives","primary_cat":"cs.LG","submitted_at":"2026-02-17T03:58:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Obj-Disco decomposes LLM alignment reward signals into sparse weighted combinations of interpretable natural language objectives via iterative analysis of behavioral changes across checkpoints, capturing over 90% of observed reward behavior.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.11318","ref_index":134,"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":"support","context_text":"the inherent biases of Western, text-based interfaces. This shift is essential for preserving the subjectivity of spoken dialects and cultural metaphors that standardized, categorical surveys often strip away [253, 289]. By adopting diverse communicative modes, the domain can better capture the richness of non-WEIRD knowledge systems. • By integrating frameworks such as Social Identity Mapping [134] and Experience-Centered AI [95] into the annotator-to-task assignment layer, the pipeline can practically surface positionality-driven lenses. 4.6.3 For Platforms and Developers: infrastructural inclusion.Platform developers must address the structural filters that currently exclude the very populations required to decolonize training data and ensure global repre-"},{"citing_arxiv_id":"2510.21293","ref_index":2,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articles","primary_cat":"cs.AI","submitted_at":"2025-10-24T09:40:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"A scoping review of AIES and FAccT literature concludes that AI trustworthiness research prioritizes technical precision over social, ethical, and institutional factors, leaving the sociotechnical nature of AI systems underexplored.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2509.12626","ref_index":14,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"DoubleAgents: Human-Agent Alignment in a Socially Embedded Workflow","primary_cat":"cs.HC","submitted_at":"2025-09-16T03:43:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"DoubleAgents shows that a distributed-cognition design with coordination agent, dashboard, and policy module increases user comfort and reliance on AI agents for coordination tasks over time.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}