Bias, Fairness and Accountability with Artificial Intelligence and Machine Learning Algorithms

Author:

Zhou Nengfeng1,Zhang Zach1ORCID,Nair Vijayan N.1ORCID,Singhal Harsh1,Chen Jie1

Affiliation:

1. Corporate Model Risk Wells Fargo San Francisco California USA

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Reference51 articles.

1. Agarwal A. Beygelzimer A. Dudik M. Langford J.&Wallach H.(2018).A reductions approach to fair classification.Proceedings of the 35th International Conference on Machine Learning PMLR (80:60‐59).

2. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

3. Barocas S. Hardt M.&Narayanan A.(2020).Fairness and machine learning.https://fairmlbook.org/

4. Barrio E. Gordaliza P.&Loubes J.(2020).Review of mathematical frameworks for fairness in machine learning.arXiv 2005.13755.

5. Berg T. Burg V. Gombović A.&Puri M.(2018).On the rise of FinTechs—credit scoring using digital footprint. NBER Working Paper No. 24551.

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