Read the text to answer the question.

     A recent Executive Order by President Biden emphasized the link between racial equity, education, and artificial intelligence (AI). It stated that the Federal Government must both pursue educational equity and eliminate bias in the design and use of new technologies, such as AI.

     The U.S. Department of Education’s report Advancing Digital Equity for All defines digital equity as the condition in which individuals and technological communities capacity needed have the for full participation in society and the economy.  

     Concerns about racial equity and bias are central to the debate on AI in education. AI systems rely on datasets, and when these datasets are non-representative or contain biased patterns, the resulting models may behave unfairly. Such systematic unfairness in automated decisions is known as algorithmic bias, which can lead to discrimination and undermine equity at scale.

     Bias is intrinsic to how AI algorithms are trained on historical data. When these biases sustain unjust or discriminatory practices in education, they must be identified and addressed. For instance, algorithms used for admissions, early intervention, or exam monitoring should be regularly evaluated for evidence of unfair bias, not only during design but also as they are deployed in real educational contexts. 

U.S. Department of Education, Office of Educational

Technology. (2023). Artificial Intelligence and the Future of

Teaching and Learning: Insights and Recommendations.

Washington, DC: U.S.

Read the text to answer the question.

     A recent Executive Order by President Biden emphasized the link between racial equity, education, and artificial intelligence (AI). It stated that the Federal Government must both pursue educational equity and eliminate bias in the design and use of new technologies, such as AI.

     The U.S. Department of Education’s report Advancing Digital Equity for All defines digital equity as the condition in which individuals and technological communities capacity needed have the for full participation in society and the economy.  

     Concerns about racial equity and bias are central to the debate on AI in education. AI systems rely on datasets, and when these datasets are non-representative or contain biased patterns, the resulting models may behave unfairly. Such systematic unfairness in automated decisions is known as algorithmic bias, which can lead to discrimination and undermine equity at scale.

     Bias is intrinsic to how AI algorithms are trained on historical data. When these biases sustain unjust or discriminatory practices in education, they must be identified and addressed. For instance, algorithms used for admissions, early intervention, or exam monitoring should be regularly evaluated for evidence of unfair bias, not only during design but also as they are deployed in real educational contexts. 

U.S. Department of Education, Office of Educational

Technology. (2023). Artificial Intelligence and the Future of

Teaching and Learning: Insights and Recommendations.

Washington, DC: U.S.

Read the text to answer the question.

     A recent Executive Order by President Biden emphasized the link between racial equity, education, and artificial intelligence (AI). It stated that the Federal Government must both pursue educational equity and eliminate bias in the design and use of new technologies, such as AI.

     The U.S. Department of Education’s report Advancing Digital Equity for All defines digital equity as the condition in which individuals and technological communities capacity needed have the for full participation in society and the economy.  

     Concerns about racial equity and bias are central to the debate on AI in education. AI systems rely on datasets, and when these datasets are non-representative or contain biased patterns, the resulting models may behave unfairly. Such systematic unfairness in automated decisions is known as algorithmic bias, which can lead to discrimination and undermine equity at scale.

     Bias is intrinsic to how AI algorithms are trained on historical data. When these biases sustain unjust or discriminatory practices in education, they must be identified and addressed. For instance, algorithms used for admissions, early intervention, or exam monitoring should be regularly evaluated for evidence of unfair bias, not only during design but also as they are deployed in real educational contexts. 

U.S. Department of Education, Office of Educational

Technology. (2023). Artificial Intelligence and the Future of

Teaching and Learning: Insights and Recommendations.

Washington, DC: U.S.

Read the text to answer the question.

     A recent Executive Order by President Biden emphasized the link between racial equity, education, and artificial intelligence (AI). It stated that the Federal Government must both pursue educational equity and eliminate bias in the design and use of new technologies, such as AI.

     The U.S. Department of Education’s report Advancing Digital Equity for All defines digital equity as the condition in which individuals and technological communities capacity needed have the for full participation in society and the economy.  

     Concerns about racial equity and bias are central to the debate on AI in education. AI systems rely on datasets, and when these datasets are non-representative or contain biased patterns, the resulting models may behave unfairly. Such systematic unfairness in automated decisions is known as algorithmic bias, which can lead to discrimination and undermine equity at scale.

     Bias is intrinsic to how AI algorithms are trained on historical data. When these biases sustain unjust or discriminatory practices in education, they must be identified and addressed. For instance, algorithms used for admissions, early intervention, or exam monitoring should be regularly evaluated for evidence of unfair bias, not only during design but also as they are deployed in real educational contexts. 

U.S. Department of Education, Office of Educational

Technology. (2023). Artificial Intelligence and the Future of

Teaching and Learning: Insights and Recommendations.

Washington, DC: U.S.