Organizational Biases

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The Risk of Machine-Learning Bias (and How to Prevent It)

Machine-learning algorithms enable companies to realize new efficiencies for tasks from evaluating credit for loan applications to scanning legal contracts for errors. But they are as susceptible as any system to the “garbage in, garbage out” syndrome when it comes to biased data. Left unchecked, feeding biased data to self-learning systems can lead to unintended and sometimes dangerous outcomes.

What Sets Breakthrough Strategies Apart

Composing valuable strategies requires seeing the world in new and unique ways. It requires asking novel questions that prompt fresh insight. Even the most sophisticated, deep learning-enhanced computers or algorithms simply cannot generate such an outlook. Innovative strategies depend more on novel, well-reasoned theories than on well-crunched numbers.

A Data-Driven Approach to Identifying Future Leaders

Many executives believe they are good at identifying leadership talent. However, when asked how they make their decisions, they often cite intuition or “gut” instincts. Social science research, on the other hand, suggests that individuals are often prone to cognitive biases in such decisions. Rather than just relying on the subjective opinions of executives, some companies are using assessment tools to identify high-potential talent.

Image courtesy of Flickr user sean dreilinger.

Why We Miss the Signs

It often seems that changes and threats come out of nowhere – until we learn later that the signals were there all along and we just didn”t read them correctly. One step toward reading them better is understanding why we misinterpret them in the first place.

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