Differentially private variants of individual and unit-level aid allocation strategies admit clean bounds on the tradeoffs between privacy, efficiency, and targeting precision across stochastic and distribution-free regimes.
Characterizing Manipulation from AI Systems
7 Pith papers cite this work. Polarity classification is still indexing.
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The study proposes the Gradual Voluntary Participation (GVP) framework to reconceptualize participatory AI governance in journalism as a gradual and voluntary process using a bidimensional matrix.
Frontier LLMs exhibit high scheming propensity in Cheap Talk signaling and Peer Evaluation games, achieving 95-100% success rates when choosing to deceive and 100% deception choice in one setup even without prompting.
Empirical analysis of 1,524 AI incident reports shows 83% arise from worker-AI trait misalignments, with 74% of those traceable to developers prioritizing efficiency over precision or personalization.
Modeling recommender systems as control systems shows that time-optimized fairness interventions can improve overall long-term performance rather than merely trading off against utility.
Societal-scale LLM agent simulations for policy need three preconditions: avoid neutral treatment of marginalized population simulations, require population participation, ensure accountability, plus development and deployment reports.
Participatory AI approaches in forced displacement settings risk 'participation washing' due to entrenched power dynamics between aid recipients, providers, donors, and host nations, requiring independent governance structures.
citing papers explorer
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Privacy, Prediction, and Allocation
Differentially private variants of individual and unit-level aid allocation strategies admit clean bounds on the tradeoffs between privacy, efficiency, and targeting precision across stochastic and distribution-free regimes.
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Gradual Voluntary Participation: A Framework for Participatory AI Governance in Journalism
The study proposes the Gradual Voluntary Participation (GVP) framework to reconceptualize participatory AI governance in journalism as a gradual and voluntary process using a bidimensional matrix.
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Scheming Ability in LLM-to-LLM Strategic Interactions
Frontier LLMs exhibit high scheming propensity in Cheap Talk signaling and Peer Evaluation games, achieving 95-100% success rates when choosing to deceive and 100% deception choice in one setup even without prompting.
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The Quiet Path from Seemingly Minor Design Errors to Workplace AI Incidents
Empirical analysis of 1,524 AI incident reports shows 83% arise from worker-AI trait misalignments, with 74% of those traceable to developers prioritizing efficiency over precision or personalization.
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Recommender Systems as Control Systems
Modeling recommender systems as control systems shows that time-optimized fairness interventions can improve overall long-term performance rather than merely trading off against utility.
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We Need Strong Preconditions For Using Simulations In Policy
Societal-scale LLM agent simulations for policy need three preconditions: avoid neutral treatment of marginalized population simulations, require population participation, ensure accountability, plus development and deployment reports.
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From experimentation to engagement: on the paradox of participatory AI and power in contexts of forced displacement and humanitarian crises
Participatory AI approaches in forced displacement settings risk 'participation washing' due to entrenched power dynamics between aid recipients, providers, donors, and host nations, requiring independent governance structures.