We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Rob Reich wears many hats: political philosopher, director of the McCoy Family Center for Ethics in Society, and associate director of the Stanford Institute for Human-Centered Artificial Intelligence. In recent years, Reich has delved deeply into the ethical and political issues posed by revolutionary technological advances in artificial intelligence (AI). His work is not always easy for technologists to hear. In his book, System Error: Where Big Tech Went Wrong and How We Can Reboot, Reich and his co-authors (computer scientist Mehran Sahami and social scientist Jeremy M. Weinstein) argued that tech companies and developers are so fixated on "optimization" that they often trample on human values.

Turning principles into practice is one of the most pressing challenges of artificial intelligence (AI) governance. In this article, we reflect on a novel governance initiative by one of the world's largest AI conferences. In 2020, the Conference on Neural Information Processing Systems (NeurIPS) introduced a requirement for submitting authors to include a statement on the broader societal impacts of their research. Drawing insights from similar governance initiatives, including institutional review boards (IRBs) and impact requirements for funding applications, we investigate the risks, challenges and potential benefits of such an initiative. Among the challenges, we list a lack of recognised best practice and procedural transparency, researcher opportunity costs, institutional and social pressures, cognitive biases, and the inherently difficult nature of the task. The potential benefits, on the other hand, include improved anticipation and identification of impacts, better communication with policy and governance experts, and a general strengthening of the norms around responsible research. To maximise the chance of success, we recommend measures to increase transparency, improve guidance, create incentives to engage earnestly with the process, and facilitate public deliberation on the requirement's merits and future. Perhaps the most important contribution from this analysis are the insights we can gain regarding effective community-based governance and the role and responsibility of the AI research community more broadly.

Similarly, institutional review boards charged with AI oversight can look to previous cases to apply their principles consistently. Let's say, for example, that your IRB declined to approve a contract with a particular country due to ethical risks related to how that government functions. It could apply the reasoning behind that decision to similar cases in the future. Additionally, if a certain case is unprecedented, an IRB can apply fictionalized scenarios to help it understand how it should apply its principles.

Conversations around AI and ethics may have started as a preoccupation of activists and academics, but now -- prompted by the increasing frequency of headlines of biased algorithms, black box models, and privacy violations -- boards, C-suites, and data and AI leaders have realized it's an issue for which they need a strategic approach. A solution is hiding in plain sight. Other industries have already found ways to deal with complex ethical quandaries quickly, effectively, and in a way that can be easily replicated. Instead of trying to reinvent this process, companies need to adopt and customize one of health care's greatest inventions: the Institutional Review Board, or IRB. Most discussions of AI ethics follow the same flawed formula, consisting of three moves, each of which is problematic from the perspective of an organization that wants to mitigate the ethical risks associated with AI. Here's how these conversations tend to go. First, companies move to identify AI ethics with "fairness" in AI, or sometimes more generally, "fairness, equity, and inclusion."