Fairkit, Fairkit, on the Wall, Who's the Fairest of Them All? Supporting Data Scientists in Training Fair Models

Understand

Modern software relies heavily on data and machine learning, and affects decisions that shape our world.

• Unfortunately, recent studies have shown that because of biases in data, software systems frequently inject bias into their decisions, from producing better closed caption transcriptions of men's voices than of women's voices to overcharging people of color for financial loans.
• To address bias in machine learning, data scientists need tools that help them understand the trade-offs between model quality and fairness in their specific data domains.
• Toward that end, we present fairkit-learn, a toolkit for helping data scientists reason about and understand fairness.