Date of Award
Fall 12-2017
Document Type
Thesis
Degree Name
Master of Science (MS)
Department/Program
Digital Forensics and Cybersecurity
Language
English
First Advisor or Mentor
Hunter Johnson
Second Reader
Ping Ji
Third Advisor
Douglas Salane
Abstract
The topic of machine ethics is growing in recognition and energy, but bias in machine learning algorithms outpaces it to date. Bias is a complicated term with good and bad connotations in the field of algorithmic prediction making. Especially in circumstances with legal and ethical consequences, we must study the results of these machines to ensure fairness. This paper attempts to address ethics at the algorithmic level of autonomous machines. There is no one solution to solving machine bias, it depends on the context of the given system and the most reasonable way to avoid biased decisions while maintaining the highest algorithmic functionality. To assist in determining the best solution, we turn to machine ethics.
Recommended Citation
Shadowen, Ashley Nicole, "Ethics and Bias in Machine Learning: A Technical Study of What Makes Us “Good”" (2017). CUNY Academic Works.
https://academicworks.cuny.edu/jj_etds/44
Included in
Applied Ethics Commons, Artificial Intelligence and Robotics Commons, Databases and Information Systems Commons, Theory and Algorithms Commons