Venkatraman Ramakrishna, Nitendra Rajput, et al.
IBM J. Res. Dev
Today, machine-learning software is used to help make decisions that affect people's lives. Some people believe that the application of such software results in fairer decisions because, unlike humans, machine-learning software generates models that are not biased. Think again. Machine-learning software is also biased, sometimes in similar ways to humans, often in different ways. While fair model- assisted decision making involves more than the application of unbiased models-consideration of application context, specifics of the decisions being made, resolution of conflicting stakeholder viewpoints, and so forth-mitigating bias from machine-learning software is important and possible but difficult and too often ignored.
Venkatraman Ramakrishna, Nitendra Rajput, et al.
IBM J. Res. Dev
Abhijit Mishra, Diptesh Kanojia, et al.
CoNLL 2016
Jason Ellis, Biplav Srivastava, et al.
AAAI 2018
Matthew Arnold, David Grove, et al.
IBM J. Res. Dev