Machine learning in medicine: Addressing ethical challenges

Practical Challenges in Explicit Ethical Machine Reasoning

We examine implemented systems for ethical machine reasoning with a view to identifying the practical challenges (as opposed to philosophical challenges) posed by the area. We identify a need for complex ethical machine reasoning not only to be multi-objective, proactive, and scrutable but that it must draw on heterogeneous evidential reasoning. We also argue that, in many cases, it needs to op...

Looking ahead: addressing ethical challenges in public health practice.

Ethical challenges in public health can have a significant impact on the health of communities if they impede efficiencies and best practices. Competing needs for resources and a plurality of values can challenge public health policymakers and practitioners to make fair and effective decisions for their communities. In this paper, the authors offer an analytic framework designed to assist polic...

Machine Learning in Medicine.

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too ...

Challenges in Statistical Machine Learning

Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives

The concept of ‘big data’ has been widely discussed, and its value has been illuminated throughout a variety of domains. To quickly mine potential values and alleviate the ever-increasing volume of information, machine learning is playing an increasingly important role and faces more challenges than ever. Because few studies exist regarding how to modify machine learning techniques to accommoda...