Material Detail

This interactive tool lets you design a simple promotion algorithm and watch how it affects different demographic groups. You'll work with a synthetic company of 240 employees who need to be evaluated for promotion. The dataset mirrors a common real-world problem: even when people have similar talent and performance, they often receive different levels of support, training, and recognition based on their demographic characteristics. These historical patterns get baked into the data that algorithms use to make decisions.

Your job is to decide how much weight the algorithm should place on different factors like performance scores, manager ratings, and training hours. As you adjust these weights, you'll see how promotion rates shift across gender and racial groups. Some weight combinations will amplify historical biases, while others might reduce disparities. The goal is to understand that algorithmic fairness isn't automatic. When we feed biased historical data into neutral-looking formulas, we often get biased outcomes. The challenge becomes deciding how to balance predictive accuracy, business needs, and equity concerns.

This will delete the comment from the database. This operation is not reversible. Are you sure you want to do it?

Thank you for reporting a broken "Go to Material" link in MERLOT to help us maintain a collection of valuable learning materials.

If you feel this material is inappropriate for the MERLOT Collection, please click SEND REPORT, and the MERLOT Team will investigate. Thank you!

You are being taken to the material on another site. This will open a new window.

Do not show me this again