Bias in the CodeAlgorithmic Fairness in Relationships

Dating app algorithms might inadvertently favor certain demographics or characteristics based on biased training data or engagement metrics that reflect societal preferences rather than individual compatibility. This could limit exposure to diverse potential partners or steer users towards homogenous pools. For instance, if an algorithm observes that users frequently swipe right on profiles with certain features reflecting societal beauty standards, it might over-represent those features, potentially sidelining deeper compatibility factors discussed in research like Gottman's work on marriage success. Research has shown that such systems can amplify the salience of race as a factor in finding intimate connections.

AI systems offering relationship advice, like some functionalities of advanced chatbots, could also exhibit bias if trained predominantly on data reflecting traditional gender roles or specific cultural norms. [12] The advice generated might not be appropriate or helpful for all users, particularly those in non-traditional relationships or from different cultural backgrounds. This connects to broader questions about the reliability of AI in giving advice.

Addressing algorithmic bias requires a multi-faceted approach. This includes diversifying datasets, developing bias detection and mitigation techniques, and ensuring transparency in how algorithms work. [4, 6, 12] Frameworks focusing on Fairness, Accountability, Transparency, and Ethics (FATE) in AI are crucial for developing systems that are just and equitable. [7, 9] Selbst et al. (2019) emphasize the importance of understanding fairness and abstraction in sociotechnical systems. [10]