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.
Creating Simple Rules for Complex Decisions
This tool can outperform human experts.
April 19, 2017
Summary.
Despite recent advances in machine learning capabilities, many people remain hesitant about letting algorithms make high-stakes decisions such as diagnosing patients and setting bail. This hesitation generally stems from the perceived difficultly of designing, applying, and understanding complex machine learning systems. To address these perceptions, the authors provide a simple three-step procedure that allows anyone to automate classification decisions: just select leading indicators, regress the outcome, and round the weights. This strategy eliminates the complexity that clouds many advanced machine learning tools while yielding clear, statistically-informed decision-making rules.
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New!
HBR Learning
Decision Making Course
Accelerate your career with Harvard ManageMentor®. HBR Learning’s online leadership training helps you hone your skills with courses like Decision Making. Earn badges to share on LinkedIn and your resume. Access more than 40 courses trusted by Fortune 500 companies.
Practical ways to improve your decision-making process.