Ethical and Security Considerations

Now, we must discuss the ethical considerations surrounding the use of AI autonomous vehicles in public transportation sectors. While delving into this subject, it raises the question of who is responsible in the event of a critical accident or system failure on the road. For example, hypothetically, if an AI vehicle were involved in a significant collision, who bears responsibility: the driver behind the wheel, the manufacturer, or the software engineer? Furthermore, considering ethical privacy concerns, should manufacturers and developers be allowed to track and store data analysis on users' driving behaviors on public roads? Location data and driving patterns are personal to public drivers who may not want their information known to the engineers of their AI vehicles. These examples share security situations where a trustworthy relationship between the producer and consumer must be established effectively. Patrick Lin, in a TED Talk about the ethical dilemma of self-driving cars, stated, "If a programmer were to instruct a car to avoid a certain obstacle with a set of code instructions, but this resulted in a fatal car accident, it would frame itself as a premeditated homicide"(Patrick Lin, 2016, Retrieved 20 Sept. 2023). He goes on to discuss how accidents like this are inevitable for both manual and self-driving vehicles. Consider a scenario where an autonomous car faces an approaching collision but must choose between colliding with another car or a person on a motorcycle. In this situation, the AI software would analyze the situation to minimize damage and choose to collide with the car, as this choice results in significantly lower injury risk compared to impacting the motorcyclist. Thus, emphasizing the urgency for software developers and manufacturers to prioritize minimal risk analysis in machine learning systems for public transportation road safety (Patrick Lin, 2016, Retrieved 20 Sept. 2023). By doing so, we can effectively address the ethical dimension of AI autonomous vehicles, particularly their decision-making processes aimed at ensuring the safety of users and others on the road. While accidents are inevitable risks on the road, the key lies in the mindset of computer specialists to reduce these occurrences through ongoing innovative and analytical capabilities of AI systems, as described in the scenario above.