no code implementations • 7 Feb 2024 • Ahmad-Reza Ehyaei, Ali Shirali, Samira Samadi
To address these issues, our work proposes a collective approach for formulating counterfactual explanations, with an emphasis on utilizing the current density of the individuals to inform the recommended actions.
no code implementations • 9 Nov 2023 • Florian E. Dorner, Tom Sühr, Samira Samadi, Augustin Kelava
With large language models (LLMs) appearing to behave increasingly human-like in text-based interactions, it has become popular to attempt to evaluate various properties of these models using tests originally designed for humans.
no code implementations • 30 Oct 2023 • Ahmad-Reza Ehyaei, Golnoosh Farnadi, Samira Samadi
Despite the essential need for comprehensive considerations in responsible AI, factors like robustness, fairness, and causality are often studied in isolation.
no code implementations • 17 Aug 2023 • Ahmad-Reza Ehyaei, Kiarash Mohammadi, Amir-Hossein Karimi, Samira Samadi, Golnoosh Farnadi
In this paper, we propose a novel approach that examines the relationship between individual fairness, adversarial robustness, and structural causal models in heterogeneous data spaces, particularly when dealing with discrete sensitive attributes.
1 code implementation • 19 Jul 2022 • Mohammad-Amin Charusaie, Hussein Mozannar, David Sontag, Samira Samadi
One of the goals of learning algorithms is to complement and reduce the burden on human decision makers.
1 code implementation • 7 May 2021 • Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar, Krishnaram Kenthapadi, Chris Russell
We initiate the study of fairness for ordinal regression.
2 code implementations • 17 Jun 2020 • Mehrdad Ghadiri, Samira Samadi, Santosh Vempala
We show that the popular k-means clustering algorithm (Lloyd's heuristic), used for a variety of scientific data, can result in outcomes that are unfavorable to subgroups of data (e. g., demographic groups).
2 code implementations • NeurIPS 2019 • Uthaipon Tantipongpipat, Samira Samadi, Mohit Singh, Jamie Morgenstern, Santosh Vempala
Our main result is an exact polynomial-time algorithm for the two-criterion dimensionality reduction problem when the two criteria are increasing concave functions.
1 code implementation • 24 Jan 2019 • Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern
Given the widespread popularity of spectral clustering (SC) for partitioning graph data, we study a version of constrained SC in which we try to incorporate the fairness notion proposed by Chierichetti et al. (2017).
1 code implementation • NeurIPS 2018 • Samira Samadi, Uthaipon Tantipongpipat, Jamie Morgenstern, Mohit Singh, Santosh Vempala
This motivates our study of dimensionality reduction techniques which maintain similar fidelity for A and B.