Presentation description

Provides an overview of the dominant approach to understanding the ethical implications of implementing machine learning applications in specific contexts, which primarily concerns itself with translating abstract principles into technical measures of compliance. While this approach can often seem very pragmatic, it is also limited in the scope of ethical dilemmas that it can address. I offer an alternative which places less emphasis on the development, or 'training, process iteslf.