Algorithmic fairness in social context

Abstract

Algorithmic fairness research is currently receiving significant attention, aiming to ensure that algorithms do not discriminate between different groups or individuals with similar characteristics. However, with the popularization of algorithms in all aspects of society, algorithms have changed from mere instruments to social infrastructure. For instance, facial recognition algorithms are widely used to provide user verification services and have become an indispensable part of many social infrastructures like transportation, health care, etc. As an instrument, an algorithm needs to pay attention to the fairness of its behavior. However, as a social infrastructure, it needs to pay even more attention to its impact on social fairness. Otherwise, it may exacerbate existing inequities or create new ones. For example, if an algorithm treats all passengers equally and eliminates special seats for pregnant women in the interest of fairness, it will increase the risk of pregnant women taking public transport and indirectly damage their right to fair travel. Therefore, algorithms have the responsibility to ensure social fairness, not just within their operations. It is now time to expand the concept of algorithmic fairness beyond mere behavioral equity, assessing algorithms in a broader societal context, and examining whether they uphold and promote social fairness. This article analyzes the current status and challenges of algorithmic fairness from three key perspectives: fairness definition, fairness dataset, and fairness algorithm. Furthermore, the potential directions and strategies to promote the fairness of the algorithm are proposed.

Keywords

• Social fairness
• Bias
• Discrimination
• Fairness measure
• Fairness datasets
• Fairness algorithms