; (B) minimization of inappropriate bias in training data sets or algorithmic feature selection; (C) qualitative and quantitative forecasting of future capabilities and applications; and (D) societal and ethical implications of artificial intelligence; (2) use existing authorities and programs and collaborate with other Federal agencies— (A) to improve teaching and learning in science and engineering with respect to artificial intelligence during the primary, secondary, undergraduate, graduate, postgraduate, adult learning, and career retraining stages of education; (B) to increase participation in artificial intelligence fields, including by individuals identified in sections 33 and 34 of the Science and Engineering Equal Opportunities Act ( 42 U.S.C. 1885a , 1885b); (C) to formulate goals for education activities in engineering and research with respect to artificial intelligence to be supported by the National Science Foundation related to topics important to the Initiative, including— (i) algorithm accountability; (ii) algorithm explainability; (iii) consumer data privacy; (iv) assessment and minimization of inappropriate bias in training data and output; (v) societal and ethical implications of the use of artificial intelligence; (vi) algorithm trustworthiness; and (vii) algorithmic forecasting; (D) to engage with institutions of higher education, research communities, potential users of information produced under this section, entities in the private sector, and non-Federal entities— (i) to leverage the collective body of knowledge from existing research and education activities with respect to artificial intelligence and artificial intelligence engineering; and (ii) to support partnerships among institutions of higher education and industry that facilitate collaborative research, personnel exchanges, and workforce development with respect to artificial intelligence and artificial intelligence engineering