Quantifying Algorithmic Biases over Time

Algorithmic bias is not static: The paper demonstrates that biases in algorithms change significantly over time, using the example of Microsoft's Tay chatbot, which was not biased at launch but "quickly became biased, sexist, and racist over time." This challenges the assumption that a single bias audit is sufficient to ensure fairness.

One-time bias measurements are insufficient: The case study tracking gender representation in Twitter image search results for #Nurse and #Doctor over 21 days found that "biases vary significantly over time and the direction of bias could appear to be different on different days," indicating that snapshot audits may miss important patterns of bias evolution.

Bias measurement requires new frameworks: The paper introduces "intuitive metrics to study the variations in biases over time," suggesting that existing bias measurement tools designed for static algorithms are inadequate and new approaches are needed to monitor algorithmic fairness continuously.

Real-world algorithms are dynamic systems: The research highlights that algorithms operate in changing environments—whether through user interactions, data updates, or model retraining—meaning bias monitoring must be treated as an ongoing process rather than a one-time compliance check.

Continuous monitoring is necessary for responsible AI: The findings suggest that organizations deploying algorithms should implement systems to track bias metrics over time rather than relying on initial fairness assessments, making ongoing oversight a key component of responsible AI deployment.