Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods

Author:

van Giffen Benjamin,Herhausen Dennis,Fahse Tobias

Publisher

Elsevier BV

Subject

Marketing

Reference65 articles.

1. Angwin, J., Larson, J., Mattu, S., and Kirchner, L. (2016). Machine bias: Retrieved from https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing. Accessed on January 6, 2022.

2. Understand, Manage, and Prevent Algorithmic Bias. A Guide for Business Users and Data Scientists;Baer,2019

3. Bias on the web;Baeza-Yates;Communications of the ACM,2018

4. Engaging the ethics of data science in practice;Barocas;Communications of the ACM,2017

5. Big Data’s Disparate Impact;Barocas;California Law Review,2016

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