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Found 5 papers, 2 papers with code
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks
Based on this observation, we investigate an alternative fairness approach: we add a second classification head to the network to explicitly predict the protected attribute (such as race or gender) alongside the original task.
Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers
Algorithmic fairness is frequently motivated in terms of a trade-off in which overall performance is decreased so as to improve performance on disadvantaged groups where the algorithm would otherwise be less accurate.
Insights into Ordinal Embedding Algorithms: A Systematic Evaluation
However, there does not exist a fair and thorough assessment of these embedding methods and therefore several key questions remain unanswered: Which algorithms perform better when the embedding dimension is constrained or few triplet comparisons are available?
Uncertainty Estimates for Ordinal Embeddings
To investigate objects without a describable notion of distance, one can gather ordinal information by asking triplet comparisons of the form "Is object $x$ closer to $y$ or is $x$ closer to $z$?"
