The Ethics of Biased Artificial Intelligence: A Stakeholder and Shareholder Theory Investigation
Document Type Thesis or dissertation
This paper aims to investigate how shareholder and stakeholder theory apply to issues of biased artificial intelligence, specifically machine learning. It is done through an event study that measures whether a significant abnormal return in share price occurs on the day of a news leak about a biased AI event, which can be used to understand how market discipline and thus shareholder theory apply in this context. A sample of ten large publicly listed US-based technology companies was used. No consistent, statistically significant abnormal returns were found, which indicates no evidence that shareholder theory is an effective mechanism for preventing and responding to issues of biased artificial intelligence. This troubling finding prompts further exploration of stakeholder theory and government regulation as methods to ensure justice and fairness in algorithmic decision making.
Date Posted: 26 September 2022