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Found 4 papers, 1 papers with code
ShaRP: Explaining Rankings with Shapley Values
Using ShaRP, we show that even when the scoring function used by an algorithmic ranker is known and linear, the weight of each feature does not correspond to its Shapley value contribution.
Fairness in Algorithmic Recourse Through the Lens of Substantive Equality of Opportunity
Building upon an agent-based framework for simulating recourse, this paper demonstrates how much effort is needed to overcome disparities in initial circumstances.
Setting the Right Expectations: Algorithmic Recourse Over Time
The bulk of the literature on algorithmic recourse to-date focuses primarily on how to provide recourse to a single individual, overlooking a critical element: the effects of a continuously changing context.
Research Trends and Applications of Data Augmentation Algorithms
In this paper we identify the main areas of application of data augmentation algorithms, the types of algorithms used, significant research trends, their progression over time and research gaps in data augmentation literature.
