Final answer:
Fairness in machine learning aims to reduce group bias but may still harm individuals within those groups by potentially reflecting and amplifying existing biases. AI can also pose risks to society such as privacy infringement and job loss. Transparency in AI and legal reforms are necessary to address these challenges.
Step-by-step explanation:
Fairness in machine learning aims to protect groups from bias, but can still harm individuals within those groups. Despite efforts to reduce bias, individual biases, such as gender biases, are key in driving discrimination. Legal reforms can influence these attitudes but cannot always rectify the intricate biases embedded in technology and AI systems.
Improvements in technology, including machine learning algorithms, have the potential to reduce disparities, such as in areas of sentencing in the criminal justice system, and to enhance societal well-being by reducing the population of minorities in jail. However, these systems can also replicate existing biases if not carefully designed and monitored.
Furthermore, AI systems pose risks such as cybercrime, infringement on privacy, and the potential job loss due to automation. Pew Research Center surveys highlight these issues, indicating a mismatch between our ability to cooperate and the manner in which we use technology. There is a need for transparency in AI development, particularly in legal aspects, to determine whether AI is more harmful or helpful to society!