Jess Whittlestone

Achieving the global benefits of artificial intelligence (AI) will require international cooperation on many areas of governance and ethical standards, while allowing for diverse cultural perspectives and priorities. There are many barriers to achieving this at present, including mistrust between cultures, and more practical challenges of coordinating across different locations. This paper focuses particularly on barriers to cooperation between Europe and North America on the one hand and East Asia on the other, as regions which currently have an outsized impact on the development of AI ethics and governance. We suggest that there is reason to be optimistic about achieving greater cross-cultural cooperation on AI ethics and governance. We argue that misunderstandings between cultures and regions play a more important role in undermining cross-cultural trust, relative to fundamental disagreements, than is often supposed. Even where fundamental differences exist, these may not necessarily prevent productive cross-cultural cooperation, for two reasons: (1) cooperation does not require achieving agreement on principles and standards for all areas of AI; and (2) it is sometimes possible to reach agreement on practical issues despite disagreement on more abstract values or principles. We believe that academia has a key role to play in promoting cross-cultural cooperation on AI ethics and governance, by building greater mutual understanding, and clarifying where different forms of agreement will be both necessary and possible. We make a number of recommendations for practical steps and initiatives, including translation and multilingual publication of key documents, researcher exchange programmes, and development of research agendas on cross-cultural topics.

This chapter assesses how advances in artificial intelligence (AI) can help us address the biggest global challenges we face today. Psychology research has painted a pessimistic picture of human decision-making in recent decades: documenting a whole host of biases and irrationalities people are prone to. We find it difficult to be motivated by long-term, abstract, or statistical considerations; many global challenges are far too complex for a human brain to understand in its entirety; and we cannot predict far into the future with any degree of certainty. At the same time, advances in AI are receiving increasing amounts of attention, raising the question: might we be able to leverage these AI developments to improve human decision-making on the problems that matter most for humanity’s future? If so, how? Thinking about AI more as supporting and complementing human decisions, than as replacing them, we might find that what we most need is quite far from the most sophisticated machine learning capabilities that are the subject of hype and research attention today. For many important real-world problems, what is most needed is not necessarily better computer vision or natural language processing but simpler ways to do large-scale data analysis, and practical tools for structuring reasoning and decision-making.