Machines can now beat humans at complex tasks that seem tailored to the strengths of the human mind, including poker, the game of Go, and visual recognition. Yet for many high-stakes decisions that are natural candidates for automated reasoning, like doctors diagnosing patients and judges setting bail, experts often favor experience and intuition over data and statistics. This reluctance to adopt formal statistical methods makes sense: Machine learning systems are difficult to design, apply, and understand. But eschewing advances in artificial intelligence can be costly. Recognizing the real-world constraints that managers and engineers face, we developed a simple three-step procedure for creating rubrics that improve yes-or-no decisions. These rubrics can help judges decide whom to detain, tax auditors whom to scrutinize, and hiring managers whom to interview. Our approach offers practitioners the performance of state-of-the-art machine learning while stripping away needless c
PRINT & PACKAGING | DIGITAL MEDIA | BRANDING | RESEARCH