CONFAIR: Configurable and Interpretable Algorithmic Fairness

Understand

The rapid growth of data in the recent years has led to the development of complex learning algorithms that are often used to make decisions in real world.

• While the positive impact of the algorithms has been tremendous, there is a need to mitigate any bias arising from either training samples or implicit assumptions made about the data samples.
• This need becomes critical when algorithms are used in automated decision making systems that can hugely impact people's lives.
• Many approaches have been proposed to make learning algorithms fair by detecting and mitigating bias in different stages of optimization.