Lessons learned from AI ethics principles for future actions

As the use of artificial intelligence (AI) systems became significantly more prevalent in recent years, the concerns on how these systems collect, use and process big data also increased. To address these concerns and advocate for ethical and responsible development and implementation of AI, non-governmental organizations (NGOs), research centers, private companies, and governmental agencies published more than 100 AI ethics principles and guidelines. This first wave was followed by a series of suggested frameworks, tools, and checklists that attempt a technical fix to issues brought up in the high-level principles. Principles are important to create a common understanding for priorities and are the groundwork for future governance and opportunities for innovation. However, a review of these documents based on their country of origin and funding entities shows that private companies from US-West axis dominate the conversation. Several cases surfaced in the meantime which demonstrate biased algorithms and their impact on individuals and society. The field of AI ethics is urgently calling for tangible action to move from high-level abstractions and conceptual arguments towards applying ethics in practice and creating accountability mechanisms. However, lessons must be learned from the shortcomings of AI ethics principles to ensure the future investments, collaborations, standards, codes or legislation reflect the diversity of voices and incorporate the experiences of those who are already impacted by the biased algorithms.