Date of Award
Master of Science (MS)
Digital Forensics and Cybersecurity
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.
Shadowen, Ashley Nicole, "Ethics and Bias in Machine Learning: A Technical Study of What Makes Us “Good”" (2017). CUNY Academic Works.