Machine learning is a powerful technology that has the potential to completely transform the way healthcare is delivered, but unlocking this can come with risks. Ethical questions should be asked in the design and implementation of machine learning models to ensure models are developed to maximize benefit and avoid potential harm. Machine learning relies on access to historical data, often containing personal information, and frequently available in lower quantity and quality than would be ideal. How to protect privacy, account for inherent bias, ensure that the right people benefit, and explain complex models are ethical challenges faced in the development of this capability. This session explores how to build ethical considerations into machine learning projects and tools. Having an ethical framework will help to provide transparency and build public trust.