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Author

Héber Hwang Arcolezi

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#artificial intelligence Preprint Sep 2026

PrivacySkills: How Privacy Guidance Shapes Source Selection in LLM Agents

PrivacySkills is introduced, a controlled framework for evaluating how agents choose among acquisition pathways that provide the same task-relevant value: consulting publicly available personal information, accessing confidential sources, or interacting with the user.

Lucas Biechy, Cédric Eichler, H. H. Arcolezi et al. · 0 citations
Review Open access Jul 2026

Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data

It is demonstrated that while DP alone can degrade both utility and fairness, applying fairness interventions can partially restore equitable outcomes, and post-processing methods tend to provide more stable fairness–utility trade-offs across privacy budgets and synthesizers.

Vinícius Gabriel Angelozzi, Héber H. Arcolezi · 1 citation · ⚡1

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