A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Saisab Sadhu, Aadit Sengupta, Vinay Kumar Sankarapu, et al.
This paper identifies 'rule blindness' in compliance detectors, where verdicts ignore the stated rule, and introduces a training-free Internal Compliance Score (ICS) for auditing.
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This paper identifies distribution shift and scale as failure modes of benchmark contamination detection, revealing a reliability gap in benchmark auditing.
Xiangning Lin, Shenzhe Zhu, Shu Yang, et al.
AISPA is a user-centric framework for auditing system prompts in commercial AI products, revealing that while protective instructions are common, problematic ones persist in 40% of products.
Chenchen Gu, Xiang Lisa Li, Rohith Kuditipudi, et al.
This paper develops statistical audits to detect prompt caching in LLM APIs, revealing privacy leakage and model architecture information.
Yijia Shao, Humishka Zope, Yucheng Jiang, et al.
Introduces an auditing framework with the Human Agency Scale to assess worker desires vs. AI capabilities for automating or augmenting occupational tasks.