arXiv AI
Jun 26
Human-AI Complementarity: A Goal for Amplified Oversight
arXiv:2510. 26518v2 Announce Type: replace Abstract: Human feedback is critical for aligning AI systems to human values.
Towards Human-AI Complementarity for AI Safety
The paper investigates human‑AI complementarity as a strategy for safer AI oversight, arguing that humans and models making different mistakes can combine judgments to produce stronger oversight signals. The authors present the largest known suite of safety‑relevant tasks (3,957 instances across 8 datasets and 6 domains) and a platform for measuring performance of humans, AI, and hybrid systems. Their study, based on 35,613 judgments from 416 raters, shows a notable complementarity effect but also finds that the tested assistance methods rarely improve over the best single source and that human raters corrected AI errors less often even as overall accuracy rose.
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arXiv Machine Learning
Jul 9
Robust Human-AI Complementarity under Uncertainty
arXiv:2607. 06656v1 Announce Type: new Abstract: Machine learning models are often intended to augment rather than replace human decision makers, by providing information that is complementary to human judgement.
arXiv Machine Learning
Oct 8
Careful Judge: Safe and Efficient Human-AI Collaborative Decision Making
The paper introduces CARE, a calibrated adaptive rectification and escalation framework that integrates AI models with human reviewers to ensure safe, human-aligned decisions. CARE continuously learns from human feedback, improving automation while reducing the need for human queries. Experiments on driving, language, and robotics datasets show a 25–81% reduction in human queries compared to baseline methods.
arXiv AI
Oct 21
When Do Humans and Machines Truly Complement Each Other? Formal Conditions and Empirical Evidence for Human-AI Synergy
arXiv:2610.27965v1 Announce Type: new Abstract: Can humans and artificial intelligence cooperate such that their combination yields greater accuracy than either agent alone? This result, termed \emph...
arXiv AI
Oct 16
Open Benchmark of AI Impact on Humans (ImpactBench): an expert-guided, open platform for the holistic evaluation of AI impact on humans
The paper introduces ImpactBench, an open, expert-guided platform designed to measure the multi-dimensional impact of generative AI on users. It evaluates AI models across psychological, physical, and social wellbeing metrics derived from domain experts and existing benchmarks, using simulated multi-turn conversations with teen and adult scenarios. Ten leading LLMs were tested on 48,540 conversations, showing 68.7% promotion of desirable behaviors but only 53.1% avoidance of harms, with an 83.3% agreement rate between automated judges and expert reviewers.
arXiv AI
Aug 13
On Benchmarking Human-Like Intelligence in Machines
arXiv:2502. 20502v2 Announce Type: replace Abstract: Recent advances in Artificial Intelligence (AI) have yielded powerful computational models that, by learning from vast amounts of human-generated data, are increasingly posited as approximate models of human cognition.
arXiv AI
Sep 10
A Translational Note on AI Safety Evaluation
The article discusses how automated red‑teaming can uncover more vulnerabilities at lower cost than human red‑teaming on AI safety benchmarks, yet this comparison conflates measurement with conclusion. It argues that benchmarks only assess harms within a predefined set, leaving a "threat‑model coverage gap" that can hide new risks, as seen in non‑English prompts. The authors suggest that evaluators from deployment contexts distinct from developers are needed to close this gap.
Hugging Face Trending Papers
Sep 23
An Open Pipeline and Dashboard for Systemic-Risk Evidence under the EU AI Act's Code of Practice
Claims about AI safety reach audiences well beyond the AI community, yet many rely on opaque evidence or static assessments, when supporting evidence is accessible at all. We present the Systemic Risk...
arXiv AI
Sep 24
An Open Pipeline and Dashboard for Systemic-Risk Evidence under the EU AI Act's Code of Practice
The paper introduces the Systemic Risk Index, an open pipeline and dashboard that aggregates evidence from 19 public AI benchmarks into four systemic‑risk categories defined by the EU GPAI Code of Practice. It evaluates 18 models using harm‑preserving perturbations and simulated deployment contexts, offering users the ability to switch between average and worst‑case aggregation and to trace each risk rating back to its benchmark evidence. The study finds that worst‑case scores can be 14 to 37 points lower than average scores, and that LLM judges agree with human graders at a level comparable to human‑human agreement.
arXiv AI
Jun 4
AICompanionBench: Benchmarking LLMs-as-Judges for AI Companion Safety
arXiv:2606. 04867v1 Announce Type: new Abstract: As AI companion platforms such as Replika and Character.
arXiv AI
Oct 21
Humans Correct Their Judgments Through Debate, but Weaker AI Models Do Not
The paper examines whether AI‑driven debate can help humans and weaker language models (LLMs) correct their judgments. Experiments show that debate improves human accuracy over prior beliefs, especially with weighted voting, but offers little benefit—and sometimes harms—accuracy for weaker LLM supervisors. The study highlights debate as a promising tool for scalable human oversight while questioning its effectiveness for fully automated AI control.
arXiv AI
Sep 2
Who Judges the Judges? A Chinese Safety QA Benchmark for Evaluating LLM Responses and Safety Judges
arXiv:2609.01210v1 Announce Type: cross Abstract: Safety benchmarks for large language models often assess the risk of a user query, although the outcome of question answering depends on whether the...