Why U.S. Efforts to Promote the Rule of Law in Afghanistan Failed

Promoting the rule of law in Afghanistan has been a major U.S. foreign policy objective since the collapse of the Taliban regime in late 2001. Policymakers invested heavily in building a modern democratic state bound by the rule of law as a means to consolidate a liberal post-conflict order. Eventually, justice-sector support also became a cornerstone of counterinsurgency efforts against the reconstituted Taliban. Yet a systematic analysis of the major U.S.-backed initiatives from 2004 to 2014 finds that assistance was consistently based on dubious assumptions and questionable strategic choices. These programs failed to advance the rule of law even as spending increased dramatically during President Barack Obama's administration. Aid helped enable rent seeking and a culture of impunity among Afghan state officials. Despite widespread claims to the contrary, rule-of-law initiatives did not bolster counterinsurgency efforts. The U.S. experience in Afghanistan highlights that effective rule-of-law aid cannot be merely technocratic. To have a reasonable prospect of success, rule-of-law promotion efforts must engage with the local foundations of legitimate legal order, which are often rooted in nonstate authority, and enjoy the support of credible domestic partners, including high-level state officials

MLSys: The New Frontier of Machine Learning Systems

Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains a significant obstacle, in large part due to the radically different development and deployment profile of modern ML methods, and the range of practical concerns that come with broader adoption. We propose to foster a new systems machine learning research community at the intersection of the traditional systems and ML communities, focused on topics such as hardware systems for ML, software systems for ML, and ML optimized for metrics beyond predictive accuracy. To do this, we describe a new conference, MLSys, that explicitly targets research at the intersection of systems and machine learning with a program committee split evenly between experts in systems and ML, and an explicit focus on topics at the intersection of the two