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Found 9 papers, 3 papers with code
Scaling Guarantees for Nearest Counterfactual Explanations
Counterfactual explanations (CFE) are being widely used to explain algorithmic decisions, especially in consequential decision-making contexts (e. g., loan approval or pretrial bail).
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Machine learning is increasingly used to inform decision-making in sensitive situations where decisions have consequential effects on individuals' lives.
Privacy Amplification by Mixing and Diffusion Mechanisms
A fundamental result in differential privacy states that the privacy guarantees of a mechanism are preserved by any post-processing of its output.
Model-Agnostic Counterfactual Explanations for Consequential Decisions
Predictive models are being increasingly used to support consequential decision making at the individual level in contexts such as pretrial bail and loan approval.
Hypothesis Testing Interpretations and Renyi Differential Privacy
These conditions are useful to analyze the distinguishability power of divergences and we use them to study the hypothesis testing interpretation of some relaxations of differential privacy based on Renyi divergence.
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences
Differential privacy comes equipped with multiple analytical tools for the design of private data analyses.
A Relational Logic for Higher-Order Programs
Relational program verification can be used for reasoning about a broad range of properties, including equivalence and refinement, and specialized notions such as continuity, information flow security or relative cost.
Programming Languages
Computer-aided verification in mechanism design
To address both concerns, we explore techniques from computer-aided verification to construct formal proofs of incentive properties.
Computer Science and Game Theory Logic in Computer Science
Higher-Order Approximate Relational Refinement Types for Mechanism Design and Differential Privacy
Unlike typical programmatic properties, it is not sufficient for algorithms to merely satisfy the property---incentive properties are only useful if the strategic agents also believe this fact.
Programming Languages Computer Science and Game Theory
