Jeffrey T. Polzer

A Aliyah Jones le costaba prestar atención a los brindis de despedida. Aunque le entristecía ver marchar a su veterana colega Anne Bank, estaba más consumida tratando de averiguar quién debía sustituirla.

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In this fictional case, a vice president of marketing and sales for the cleaning division of a global consumer products company is struggling to make a promotion decision. She has two strong internal candidates. One is a brand manager on her team with whom she&#8217;s worked closely over the past few years. The other is a rising star from a different division who was recommended by the company&#8217;s new people analytics program. Both seem equally qualified, though the vice president knows one candidate far better and is therefore worried that she might be biased, in part because&#160;the candidate is also a woman. With the decision looming, she has to decide whether to trust her intuition or the firm&#8217;s algorithm.

Case Study: Should an Algorithm Tell You Who to Promote?

En este caso ficticio, una vicepresidenta de marketing y ventas de la división de limpieza de una empresa mundial de productos de consumo tiene que tomar una decisión sobre un ascenso. Tiene dos candidatas internas fuertes. Uno es un director de marca de su equipo con el que ha trabajado estrechamente en los últimos años. La otra es una estrella en ascenso de una división diferente que fue recomendada por el nuevo programa de análisis de personal de la empresa. Ambas parecen igualmente cualificadas, aunque la vicepresidenta conoce mucho mejor a una de las candidatas y por eso le preocupa que pueda ser parcial, en parte porque la candidata también es mujer. Ante la inminente decisión, tiene que decidir si confiar en su intuición o en el algoritmo de la empresa.

Estudio de caso: &iquest;Debe un algoritmo decirle a qui&eacute;n ascender?

In this fictional case, a vice president of marketing and sales for the cleaning division of a global consumer products company is struggling to make a promotion decision. She has two strong internal candidates. One is a brand manager on her team with whom she’s worked closely over the past few years. The other is a rising star from a different division who was recommended by the company’s new people analytics program. Both seem equally qualified, though the vice president knows one candidate far better and is therefore worried that she might be biased, in part because the candidate is also a woman. With the decision looming, she has to decide whether to trust her intuition or the firm’s algorithm.

Estudio de caso: ¿Debe un algoritmo decirle a quién ascender?

Resumen.

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