The General Services Administration is modernizing how agencies review regulations using machine learning (ML), in a procurement through its Centers of Excellence (CoE) initiative. GSA awarded a $9.9 million contract to Deloitte and to Esper, Inc. for ML support for agencies. ML can review rules and regulations to identify trends in the data, which can help eliminate redundancies and streamline the process of writing new ones. Both the CoEs within GSA's Technology Transformation Services and the Federal Systems Integration and Management Center (FEDSIM) have used ML, a subset of artificial intelligence, to conduct regulatory reviews. The contract extends their work to CoE partner agencies.

Federal guidance on artificial intelligence needs additions to ensure the U.S. has a seat at the international table. The rapid proliferation of applications of artificial intelligence and machine learning--or AI, for short--coupled with the potential for significant societal impact has spurred calls around the world for new regulation. The European Union and China are developing their own rules, and the Organization for Economic Cooperation and Development has developed principles that enjoy the support of its members plus a handful of other countries. In January, the U.S. Office of Management and Budget (OMB) also issued its own draft guidance, ensuring the United States a seat at the table during this ongoing, multi-year, international conversation. The U.S. guidance--covering "weak" or narrow AI applications of the kind we experience today--reflects a light-touch approach to regulation, consistent with a desire to reward U.S. ingenuity.

Compliance is a must-do activity, not a nice-to-have. It is essential that companies extract maximum value from compliance processes, reducing the possibility of it being considered a cost centre. Technological innovation can help to lift some of the compliance burden. The level of technology you can realistically implement depends on how advanced the organisation is to start with. One company's moonshot could be another's business as usual.

A division within the U.S. Food and Drug Administration (FDA), the Center for Drug Evaluation and Research (CDER) has currently seven RPA (Robotic Process Automation) projects in development as it works to free up staff for its core science mission. The center has used RPA for a year with plans to implement bots to Machine Learning and Natural Language Processing (NLP) for applications in regulatory review. CDER ensures safe and effective drugs on the market to improve the health of the people throughout their lifecycle. While the FDA is recognized in the RPA space for automating drug intake forms and work within its chief financial officer's office, CDER has quietly put several RPA use cases into production enterprise-wide. It regulates over-the-counter and prescription drugs, including biological therapeutics and generic drugs.

Scholar argues that FDA should reform its regulation of algorithm-based medicine. In emergency situations, doctors have little time to save the lives of trauma patients. Gunshot wounds, car crashes, and other life-threatening harms often cause severe blood loss, which is the leading cause of preventable death when trauma puts patients' lives on the line. To manage the demands of these emergency cases, physicians today complement their medical skill-set with a new tool: algorithms. But in a recent paper, a legal scholar argues that federal regulatory reforms must occur to unleash the full lifesaving potential of algorithms in health care.

University of Pennsylvania workshop addresses potential biases in the predictive technique. Stephen Hawking once warned that advances in artificial intelligence might eventually "spell the end of the human race." And yet decision-makers from financial corporations to government agencies have begun to embrace machine learning's enhanced power to predict--a power that commentators say "will transform how we live, work, and think." During the first of a series of seven Optimizing Government workshops held at the University of Pennsylvania Law School last year, Aaron Roth, Associate Professor of Computer and Information Science at the University of Pennsylvania, demystified machine learning, breaking down its functionality, its possibilities and limitations, and its potential for unfair outcomes. Chairman of the Penn Department of Criminology Richard Berk offers commentary. Machine learning, in short, enables users to predict outcomes using past data sets, Roth said.

The Optimizing Government Project brings together scholars and researchers to discuss the use of machine learning by government. In recent years, the private sector has succeeded in finding many ways to leverage machine learning--a type of artificial intelligence that enables computers to "learn and adapt through experience." Well-known private sector applications of machine learning include Google's self-driving car project, online recommendations personalized for customers on websites like Amazon and Netflix, and fraud detection by credit card companies. But as the private sector embraces machine learning in new ways, the application of machine learning by government agencies has only started to take root. The use of artificial intelligence by government, though, raises important questions for a democratic society--about fairness, equality, transparency, and accountability.