Description: Temporal equity in artificial intelligence (AI) systems refers to the need to ensure that algorithms and AI models maintain fair and equitable treatment over time, considering that both social contexts and the data used to train them can change. This concept implies that automated decisions should not only be fair at a specific moment but also adapt to the evolving social and economic dynamics. Temporal equity seeks to prevent historical biases from being perpetuated over time, ensuring that AI systems do not discriminate against specific groups due to outdated data or changes in social conditions. This is particularly relevant in various sectors such as hiring, criminal justice, and healthcare, where AI-based decisions can significantly impact people’s lives. Temporal equity also implies the need for continuous monitoring and adjustment of AI models to reflect current realities rather than just those of the past, thus promoting a more inclusive and fair approach to the implementation of advanced technologies.<\/p>\n","protected":false},"excerpt":{"rendered":"
Description: Temporal equity in artificial intelligence (AI) systems refers to the need to ensure that algorithms and AI models maintain fair and equitable treatment over time, considering that both social contexts and the data used to train them can change. This concept implies that automated decisions should not only be fair at a specific moment […]<\/p>\n","protected":false},"author":1,"featured_media":0,"menu_order":0,"comment_status":"open","ping_status":"open","template":"","meta":{"footnotes":""},"glossary-categories":[],"glossary-tags":[],"glossary-languages":[],"class_list":["post-305975","glossary","type-glossary","status-publish","hentry"],"post_title":"Temporal Fairness ","post_content":"Description: Temporal equity in artificial intelligence (AI) systems refers to the need to ensure that algorithms and AI models maintain fair and equitable treatment over time, considering that both social contexts and the data used to train them can change. This concept implies that automated decisions should not only be fair at a specific moment but also adapt to the evolving social and economic dynamics. Temporal equity seeks to prevent historical biases from being perpetuated over time, ensuring that AI systems do not discriminate against specific groups due to outdated data or changes in social conditions. This is particularly relevant in various sectors such as hiring, criminal justice, and healthcare, where AI-based decisions can significantly impact people's lives. Temporal equity also implies the need for continuous monitoring and adjustment of AI models to reflect current realities rather than just those of the past, thus promoting a more inclusive and fair approach to the implementation of advanced technologies.","yoast_head":"\n