Fairness in Artificial Intelligence is a growing issue in new technologies today. This paper outlines apparent social problems in Artificial Intelligence (AI) and Machine Learning (ML) and possible solutions. One solution is to provide a comprehensive guide to computer scientists and software engineers on how to integrate fairness in ML. The guide covers data collection process, pre-processing data, in-processing of the algorithm, and post-processing on how the end user is effected.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Tessier, Marie

Subject

• Computing
• Inclusion

Publisher

• Worcester Polytechnic Institute

Identifier

• 69876
• E-project-060122-001611

Keyword

• Inclusive Artificial Intelligence
• Fairness In Machine Learning

Advisor

• Smith, Therese Mary

Year

• 2022

UN Sustainable Development Goals

• Not Specified

Date created

• 2022-06-01

Resource type

• Interactive Qualifying Project

Source

• E-project-060122-001611

Rights statement

• In Copyright

Last modified

• 2023-01-13