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Found 13 papers, 3 papers with code
Why am I Still Seeing This: Measuring the Effectiveness Of Ad Controls and Explanations in AI-Mediated Ad Targeting Systems
Our study evaluates the effectiveness of Meta's "See less" ad control and the actionability of ad targeting explanations following the shift to AI-mediated targeting.
Stability and Multigroup Fairness in Ranking with Uncertain Predictions
However, when predictors trained for classification tasks have intrinsic uncertainty, it is not obvious how this uncertainty should be represented in the derived rankings.
Fairness in Matching under Uncertainty
The prevalence and importance of algorithmic two-sided marketplaces has drawn attention to the issue of fairness in such settings.
"You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learning
As in traditional machine learning models, models trained with federated learning may exhibit disparate performance across demographic groups.
Robust Allocations with Diversity Constraints
We show in a formal sense that the Nash Welfare rule that maximizes product of agent values is uniquely positioned to be robust when diversity constraints are introduced, while almost all other natural allocation rules fail this criterion.
The power of synergy in differential privacy: Combining a small curator with local randomizers
Motivated by the desire to bridge the utility gap between local and trusted curator models of differential privacy for practical applications, we initiate the theoretical study of a hybrid model introduced by "Blender" [Avent et al.,\ USENIX Security '17], in which differentially private protocols of n agents that work in the local-model are assisted by a differentially private curator that has access to the data of m additional users.
Advances and Open Problems in Federated Learning
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
Preference-Informed Fairness
We introduce and study a new notion of preference-informed individual fairness (PIIF) that is a relaxation of both individual fairness and envy-freeness.
The Power of The Hybrid Model for Mean Estimation
We explore the power of the hybrid model of differential privacy (DP), in which some users desire the guarantees of the local model of DP and others are content with receiving the trusted-curator model guarantees.
Differentially-Private "Draw and Discard" Machine Learning
In this work, we propose a novel framework for privacy-preserving client-distributed machine learning.
Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12
We discover and describe Apple's set-up for differentially private data processing, including the overall data pipeline, the parameters used for differentially private perturbation of each piece of data, and the frequency with which such data is sent to Apple's servers.
BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model
We propose a hybrid model of differential privacy that considers a combination of regular and opt-in users who desire the differential privacy guarantees of the local privacy model and the trusted curator model, respectively.
RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
Randomized Aggregatable Privacy-Preserving Ordinal Response, or RAPPOR, is a technology for crowdsourcing statistics from end-user client software, anonymously, with strong privacy guarantees.
Cryptography and Security
