Article by Stuart McLennan, Amelia Fiske, Leo Anthony Celi, Ruth Müller, Jan Harder, Konstantin Ritt, Sami Haddadin and Alena Buyx. Published in Nature Machine Intelligence.
Ethical concerns around artificial intelligence (AI) technology have prompted a rush towards ‘AI ethics’ to consider how AI technology can be developed and implemented in an ethical manner. A recent scoping review identified 84 documents containing ethical principles or guidelines for AI that have been issued by a wide range of public and private organizations. In the absence of legally enforceable regulations, those developing AI technology are largely left to translate the existing high-level ethical principles as they see fit.
Furthermore, it has recently been argued that a principled approach is unlikely to be successful in AI because AI development “lacks common aims and fiduciary duties, professional history and norms, proven methods to translate principles into practice, and robust legal and professional accountability mechanisms” compared to professions like medicine
Although it is clear that a growing number of technology developers are willing to consider the ethical challenges around AI, most do not have the necessary competency to translate high-level ethical principles into practice. This is unsurprising as the professional backgrounds of AI developers usually do not include systematic training in ethics. Conversely, few trained ethicists or social scientists currently work in tech companies, and there is no established culture of practical exchange between these fields.
This creates a gap when it comes to translating ethical considerations into ethical practices, and there is a need to develop more concrete approaches. It is imperative that the ethical challenges of AI are addressed as early as possible during the development process to ensure the ethically, socially and legally responsible design and implementation of these applications. While various suggestions have emerged, so far there is no cohesive approach to integrating ethics into the development of AI and capitalize on the potential of including ethics upstream in the development process. We propose that an ‘embedded ethics’ approach can fill this gap and promote a more ethical development of AI applications. [ . . . ]