Machine Excellence Tradeoffs to Ethical and Legal Perspectives

Abstract

We appreciate well-functioning technology being able to also personalize its services. However, to protect privacy and avoid a potential misuse of personal data, we are encouraged to limit the amount of personal data we share through apps and Internet services. While some services do not really need all the data they ask us to provide, others depend on it to provide the best possible performance of its service. That regards systems that apply data in machine learning for tasks like medical diagnostics. Especially deep learning algorithms perform better by using a large amount of data and are now able to benefit from the large amount as well with limited training time given access to high-performance computing resources. This paper address and discuss the tradeoffs like the one we have between data sharing minimalization for increased privacy and data maximization for machine learning systems. Perspectives related to ethics, legal, and social issues are considered in the paper. There is no single conclusion on the challenge, but attention to it can increase the awareness that the best balance differs depending on the application addressed.