Description: Temporal bias refers to the distortion that occurs when the data used to train an artificial intelligence (AI) system is not representative of the current context or time. This phenomenon can arise when historical data is used to make predictions or decisions in an environment that has changed significantly. As a result, AI models may perpetuate errors or biases that were relevant in the past but no longer apply in the present. Temporal bias can manifest in various forms, such as the underrepresentation of certain demographic groups or the overestimation of trends that have ceased to be valid. This type of bias is particularly concerning in critical applications, such as hiring, criminal justice, and healthcare, where decisions based on biased data can have serious consequences for the affected individuals. Identifying and mitigating temporal bias is essential to ensure that AI systems are fair, accurate, and ethically responsible, thereby promoting greater trust in technology and its applications in modern society.
