Machine learning and AI are effective parts of many compliance programs, and a new initiative aims to foster cooperation among companies to better train machine learning algorithms to detect, for example, transactions and third parties that are at a high risk for corruption. In the first article in this two-part series originally published in our sister publication the Anti-Corruption Report, we looked at how the initiative, called C2CRIGHT and spearheaded by Matt Galvin, global vice president, ethics and compliance at AB InBev, will work. In this second article, we uncover some of the barriers to participating in such a project and how Galvin and his team plan to alleviate those concerns. See our three-part series on AI for compliance: “Foundations” (Nov. 11, 2020); “Building a Model” (Dec. 16, 2020); and “Five Workarounds for Asymmetric Data Sets”​​​​​​ (Mar. 10, 2021).