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dalex: Responsible Machine Learning with Interactive Explainability and
  Fairness in Python

dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python

28 December 2020
Hubert Baniecki
Wojciech Kretowicz
Piotr Piątyszek
J. Wiśniewski
P. Biecek
    FaML
ArXivPDFHTML

Papers citing "dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python"

9 / 9 papers shown
Title
Machine Learning Fairness in House Price Prediction: A Case Study of America's Expanding Metropolises
Machine Learning Fairness in House Price Prediction: A Case Study of America's Expanding Metropolises
Abdalwahab Almajed
Maryam Tabar
Peyman Najafirad
AI4TS
26
0
0
02 May 2025
Properties of fairness measures in the context of varying class imbalance and protected group ratios
Properties of fairness measures in the context of varying class imbalance and protected group ratios
D. Brzezinski
Julia Stachowiak
Jerzy Stefanowski
Izabela Szczech
R. Susmaga
Sofya Aksenyuk
Uladzimir Ivashka
Oleksandr Yasinskyi
131
4
0
13 Nov 2024
EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders
EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders
Lin Luo
Yuri Nakao
Mathieu Chollet
Hiroya Inakoshi
Simone Stumpf
35
0
0
16 Jul 2024
Evaluating quantum generative models via imbalanced data classification
  benchmarks
Evaluating quantum generative models via imbalanced data classification benchmarks
Graham Enos
M. Reagor
Eric Hulburd
23
0
0
21 Aug 2023
Function Composition in Trustworthy Machine Learning: Implementation
  Choices, Insights, and Questions
Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions
Manish Nagireddy
Moninder Singh
Samuel C. Hoffman
Evaline Ju
K. Ramamurthy
Kush R. Varshney
22
1
0
17 Feb 2023
DC-Check: A Data-Centric AI checklist to guide the development of
  reliable machine learning systems
DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems
Nabeel Seedat
F. Imrie
M. Schaar
27
12
0
09 Nov 2022
Explanations Based on Item Response Theory (eXirt): A Model-Specific
  Method to Explain Tree-Ensemble Model in Trust Perspective
Explanations Based on Item Response Theory (eXirt): A Model-Specific Method to Explain Tree-Ensemble Model in Trust Perspective
José de Sousa Ribeiro Filho
Lucas F. F. Cardoso
R. Silva
Vitor Cirilo Araujo Santos
Nikolas Carneiro
Ronnie Cley de Oliveira Alves
13
4
0
18 Oct 2022
OmniXAI: A Library for Explainable AI
OmniXAI: A Library for Explainable AI
Wenzhuo Yang
Hung Le
Tanmay Laud
Silvio Savarese
S. Hoi
SyDa
21
39
0
01 Jun 2022
Improving fairness in machine learning systems: What do industry
  practitioners need?
Improving fairness in machine learning systems: What do industry practitioners need?
Kenneth Holstein
Jennifer Wortman Vaughan
Hal Daumé
Miroslav Dudík
Hanna M. Wallach
FaML
HAI
192
742
0
13 Dec 2018
1