On May 30, the Center for AI Safety released a public warning of the risk artificial intelligence poses to humanity. The one-sentence statement signed by more than 350 scientists, business executives and public figures asserts: "Mitigating the risk of extinction from A.I. should be a global priority alongside other societal scale risks such as pandemics and nuclear war." It is hard not to sense the brutal double irony in this declaration. First, some of the signatories – including the CEOs of Google DeepMind and OpenAI – warning about the end of civilisation represent companies that are responsible for creating this technology in the first place. Second, it is exactly these same companies that have the power to ensure that AI actually benefits humanity, or at the very least does not do harm.

On the other hand, these research insights are meant to intervene on platforms that are globally present, serving a global population from diverse societies, cultures and values, with their own forms of injustices. A core concern in this arrangement is that of value imposition, where local values, i.e., values that are local to the regions where the interventions are built, implicitly shape and inform global systems without any or much room for discussion or contestation from those affected by those interventions. More specifically, interventions designed to address FATE failures necessarily impart a normative value system, but the values that guide the proposed solutions are rarely recognized as sites of contestation. This is problematic because while there may be ethical principles for ML that garner a degree of consensus across different value systems, in a pluralistic world this consensus is not something that should be assumed. Instead, we need to be explicit about the values that underpin the quest for ethical and just AI, and to cultivate an active debate about those values, critically examining and evaluating claims about them[28]. Another shortcoming of not being explicit about what normative value systems shape the interventions is the vagueness it entails, making it harder to arrive at a common vocabulary and shared understanding between computer scientists and civil society. Such a shared understanding is crucial to bridge the gap between research and practice, especially in a way that effectively supports the priorities of the latter constituency.

Artificial intelligence (AI)--and the big data business models underpinning it--is disrupting how we live, work, do business, and govern. The economic, social, and environmental benefits of AI could be significant, such as improved health diagnostics, self-driving vehicles that increase road safety, and enhanced fraud prevention. However, AI also brings social risks, including new forms of discrimination arising from algorithmic bias, labor impacts associated with the displacement of workers by machines, and the heightened potential of surveillance using tracking devices and facial recognition tools. Artificial intelligence (AI)--and the big data business models underpinning it--is disrupting how we live, work, do business, and govern. The speed, complexity, and novelty of these disruptions imply that similarly innovative approaches to responsible business will be needed for us to realize the full potential of AI to create long-term value.