Online and Customizable Fairness-aware Learning

Understand

While artificial intelligence (AI)-based decision-making systems are increasingly popular, significant concerns on the potential discrimination during the AI decision-making process have been observed.

• For example, the distribution of predictions is usually biased and dependents on the sensitive attributes (e.g., gender and ethnicity).
• Numerous approaches have therefore been proposed to develop decision-making systems that are discrimination-conscious by-design, which are typically batch-based and require the simultaneous availability of all the training data for model learning.
• However, in the real-world, the data streams usually come on the fly which requires the model to process each input data once ``on arrival'' and without the need for storage and reprocessing.