; Whereas AI systems have the potential to amplify and reinforce existing biases and inequalities if they fail to consider inclusion and diverse populations in AI design and perpetuate biases and inequalities; Whereas AI relies on the collection of algorithmic data that is chosen by the humans who design these algorithms, making it extraordinarily easy for humans’ own unconscious biases to enter machine learning models, leading to racially biased AI systems that recommend and drive decisions that perpetuate historical social inequalities amongst marginalized groups; Whereas the world has already seen the potential for AI algorithms to neglect and exacerbate race-based disparities, as demonstrated by the statistically significant and heavily documented racial biases identified in many widely used facial recognition algorithms; Whereas research conducted by respected institutions such as the Institute of Electrical and Electronics Engineers, the Massachusetts Institute of Technology, Cornell University, and others has shown that these algorithms exhibit significant accuracy disparities, working more effectively on White faces while frequently misidentifying or failing to recognize brown, Black, Indigenous, and darker skinned faces; Whereas ensuring the inclusion and representation of underrepresented populations is crucial in the development of AI to protect all children and democracy, and create a more inclusive society while avoiding the demonstrated pitfalls of AI algorithms that fail to account for diversity in their inputs; Whereas United States funding for the development of AI must prioritize algorithms that source data from inputs and models that account for diversity to include the experiences of underserved populations in order to promote social equity and economic justice, and ensure that such communities reap the benefits of diversity in the coming age of AI