1 citations · 1 across the 4 of their papers we have counts for
4 papers · 1 filter
Source Attribution in Retrieval-Augmented Generation
Ikhtiyor Nematov, Tarik Kalai, Elizaveta Kuzmenko +4
While attribution methods, such as Shapley values, are widely used to explain the importance of features or training data in traditional machine learning, their application to Larg…
AIDE: Antithetical, Intent-based, and Diverse Example-Based Explanations
Ikhtiyor Nematov, Dimitris Sacharidis, Tomer Sagi +1
For many use-cases, it is often important to explain the prediction of a black-box model by identifying the most influential training data samples. Existing approaches lack customi…
FALE: Fairness-Aware ALE Plots for Auditing Bias in Subgroups
Giorgos Giannopoulos, Dimitris Sacharidis, Nikolas Theologitis +2
Fairness is steadily becoming a crucial requirement of Machine Learning (ML) systems. A particularly important notion is subgroup fairness, i.e., fairness in subgroups of individua…
Auditing for Spatial Fairness
Dimitris Sacharidis, Giorgos Giannopoulos, George Papastefanatos +1
This paper studies algorithmic fairness when the protected attribute is location. To handle protected attributes that are continuous, such as age or income, the standard approach i…