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Black-Box Access is Insufficient for Rigorous AI Audits
The effectiveness of an audit, however, depends on the degree of system access granted to auditors.
Recommending to Strategic Users
We model this user behavior as a two-stage noisy signalling game between the recommendation system and users: the recommendation system initially commits to a recommendation policy, presents content to the users during a cold start phase which the users choose to strategically consume in order to affect the types of content they will be recommended in a recommendation phase.
Opaque Contracts
In a described contract, the principal sorts the agents into groups, and to each group communicates a distribution of output-contingent payments.
Towards Psychologically-Grounded Dynamic Preference Models
Second, we discuss implications of dynamic preference models for recommendation systems evaluation and design.
Risk Preferences of Learning Algorithms
The first method requires the algorithm to reweight data as a function of how likely the actions were to be chosen.
Contextually Private Mechanisms
Our first main result is a characterization of choice rules that can be implemented without producing any contextual privacy violations.
Prior-Independent Auctions for the Demand Side of Federated Learning
Federated learning (FL) is a paradigm that allows distributed clients to learn a shared machine learning model without sharing their sensitive training data.
Classification on Large Networks: A Quantitative Bound via Motifs and Graphons
When each data point is a large graph, graph statistics such as densities of certain subgraphs (motifs) can be used as feature vectors for machine learning.
