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

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.

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