The paper introduces a multi‑criteria framework for evaluating sociotechnical interventions, moving beyond sole effectiveness to include political feasibility, user acceptance, cost, and implementation effort. Using misinformation as a case study, the authors surveyed 39 researchers on 40 interventions across these five criteria, revealing that the most effective solutions are not always the most acceptable or feasible. The study offers a decision framework to help practitioners balance these trade‑offs in designing interventions.
arXiv:2607. 26236v1 Announce Type: cross Abstract: AI-generated counterspeech offers a scalable and effective strategy to mitigate online toxicity by promoting more constructive dialogue.
arXiv:2607. 05363v1 Announce Type: new Abstract: Personal agents are becoming persistent user-owned intermediaries: they remember preferences, filter platform-mediated information, use tools, and negotiate with services.
The paper surveys how automated research systems—used for literature synthesis, ideation, workflows, writing, peer review, and end‑to‑end research—are evaluated. It compares task construction, evidence sources, evaluators, and scoring methods across six target areas, revealing that output checks, process checks, and human studies together provide a fuller picture. The authors identify gaps in diagnostic designs and offer reporting and audit recommendations to guide future evaluation efforts.
arXiv:2603. 11001v3 Announce Type: replace-cross Abstract: Human uplift studies, or studies that measure the effects of AI access on human performance via randomized controlled trials (RCT) or similar methodologies, increasingly inform frontier AI governance and deployment decisions.
arXiv:2606. 05029v1 Announce Type: new Abstract: Controlled experiments are the backbone of machine learning research, but at the scale of modern foundation models, they have become prohibitively expensive.
arXiv:2605. 27395v2 Announce Type: replace-cross Abstract: As the rapid proliferation of AI systems and harms spurs efforts in AI governance around the world, prioritizing among competing policy options has become increasingly challenging for policymakers and researchers.
arXiv:2608. 15131v1 Announce Type: new Abstract: Digital platforms govern by changing rules: rankings, monetization thresholds, moderation standards, verification systems, disclosure requirements, appeal processes, and access policies.
arXiv:2607. 01250v1 Announce Type: cross Abstract: Sociotechnical alignment concerns the social desirability of AI behavior and is thus inherently normative, not merely technical.
The paper argues that fairness failures in generative models arise mainly from inadequate evaluation practices, making fairness findings hard to compare or use for deployment. It diagnoses common empirical and conceptual shortcomings in current methods and calls for a move toward standardized, generative‑specific evaluation. The authors introduce Fairness Cards, a minimal reporting artifact that explicitly documents evaluation choices—such as prompt families, counterfactual protocols, metrics, and refusal handling—to improve reproducibility, comparability, and accountability.
Despite groundbreaking advancements in generative models during the last decade, concerns about their lack of fairness, reinforcing societal inequalities and harming marginalized groups, remain under-...
arXiv:2609.00808v1 Announce Type: cross Abstract: As far-right actors increasingly exploit online platforms to disseminate ideology and mobilize supporters, civil society organizations (CSOs) play a...
The paper examines how public institutions, particularly a Canadian college, can address inequities in proprietary Early Warning Systems (EWS) when they cannot modify the underlying AI models. By testing six post‑hoc fairness interventions on a research EWS under realistic procurement constraints, the authors compare fairness, accuracy, and demographic disparities, and introduce error‑type profiling to track how interventions shift false positives and false negatives. The results show that while interventions can redistribute disparities, they do not consistently reduce them; some favor already‑advantaged groups, leaving small, marginalized groups poorly served, illustrating the limits of fairness work under vendor‑controlled systems.