Humans reach 64.8% accuracy detecting synthetic legal evidence images overall but drop to chance levels on top generators, while MLLMs achieve 100% specificity yet only 5.9% detection on the hardest synthetics, with uncorrelated error patterns.
the language of the computer: a bilingual model of legal education in the age of technology and artificial intelligence , volume =
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EvalAI providing pro/con arguments improves provision-level accuracy and reduces misclassification distance in DSA illegal content reporting under AI error conditions versus conventional XAI.
WISCA produces consensus explanations on synthetic tabular datasets that align with the most reliable individual interpretability method among those tested.
A new framework grades levels of inference capability in data-driven systems to assess compliance with the EU AI Act definition of AI, illustrated via credit scoring workflows.
citing papers explorer
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Can You Trust What You See? Human and AI Detection of Synthetic Legal Evidence
Humans reach 64.8% accuracy detecting synthetic legal evidence images overall but drop to chance levels on top generators, while MLLMs achieve 100% specificity yet only 5.9% detection on the hardest synthetics, with uncorrelated error patterns.
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AI at the Front Lines of Platform Governance: Using LLMs to Support Illegal Content Reporting under the Digital Services Act
EvalAI providing pro/con arguments improves provision-level accuracy and reduces misclassification distance in DSA illegal content reporting under AI error conditions versus conventional XAI.
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WISCA: A Consensus-Based Approach to Harmonizing Interpretability in Tabular Datasets
WISCA produces consensus explanations on synthetic tabular datasets that align with the most reliable individual interpretability method among those tested.
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When Do Data-Driven Systems Exhibit the Capability to Infer?
A new framework grades levels of inference capability in data-driven systems to assess compliance with the EU AI Act definition of AI, illustrated via credit scoring workflows.