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.
In Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization
5 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.
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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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.