The Automation and Artificial Intelligence trends have been a tool of great value for various industries. The ability to accomplish a specific task with minimum instructions from humans is made possible using the machine learning algorithm. But the rapidly growing incorporation of Machine Learning in finance, medicine, and healthcare has raised a large range of ethical concerns.
Many artificial intelligence experts from academia and industry convened at Northeastern University to discuss the challenges of integrating machine learning and the prospects of improving its productivity. This panel discussion, titled “Machine Learning and Industry—the Good, the Bad, and the Ugly,” was part of New England Machine Learning Day, an annual conference sponsored by Microsoft Research.
Carla Brodley, dean of the Khoury College of Computer Sciences at Northeastern University, says that “There’s a lot of issues that are cropping up in machine learning, now that it’s being widely used across many different sectors, In particular, there are these issues around ethics, fairness, and bias that are really interesting.”
Machine learning has already reached a point where it is very essential to every branch of science and technology. Thus there’s also a greater sense of responsibility that comes with that. Researchers use a data set to train machine learning algorithms. So if the data is biased there are chances that the algorithm will also make biased decisions and reinforce them. The only way to ensure ethics, in this case, is to make sure that data has enough diversities and representation.
Researchers say that they need to work on eliminating the bias in the algorithm or more precisely communicate the limitations of machine learning to the public. Their motive should shift towards building more systems that would prioritize human health and welfare.