Black girls as creators in the field of AI

The challenge

Black girls are rarely seen as creators in the field of artificial intelligence (AI). Instead, they face double jeopardy due to their intersecting identities (such as race and gender) resulting in the experiences of racism, sexism and other oppressions simultaneously which are often unaddressed. 

Current approaches to AI education do not consider how curricula and integrated technologies, and facilitator professional development, work in tandem to support learners likely limiting the effectiveness of any individual component. 

Research indicates that Black students often experience racism in K–12 educational systems including access to AI curriculum and learning. The marginalization, limitations and lowered chances for success in schools for Black students can be attributed to structural racism.

The approach

The project, Black girls as creators: an intersectional learning ecosystem toward gendered racial equity in artificial intelligence education, is spearheaded by MLFTC Assistant Professor Tara Nkrumah and funded by the National Science Foundation. The research is being conducted in two community organizations in Pittsburgh and aims to conceptualize an evidence-based intersectional AI learning ecosystem that enhances fourth through eighth-grade Black girls’ engagement with AI education while advancing out-of-school facilitators’ critical consciousness to challenging systemic gendered racism. 

The researchers are working with the Manchester Youth Development Center whose out-of-school programming emphasizes empowering learners not just as consumers of technology, but as creators. The center’s programming combines technical and creative activities to expose learners to the variety of ways they can engage in computing now, and in a future career. Its population is 99% Black. The other organization Nkrumah and her team is working with is Assemble, which serves primarily Black, Latinx and Asian learners. Assemble’s programming emphasizes community and creativity with the goal of nurturing learners’ agency and self-expression. 

Through co-design with Black girls, the project aims to disrupt systemic barriers to Black girls. The learning ecosystem consists of AI curricula and integrated technologies that work together to support Black girls’ creation in AI spaces rather than reinforcing dominant norms in computing education that train Black girls to be competent technology workers. 

The researchers’ objective is to spotlight how Black girls and their creativity can inform the operationalization of gendered racial equity in out-of-school AI learning ecosystems and future AI education. This focus attends to gaps in the research literature pertaining to gendered racialized experiences of Black girls in AI learning. The project has the potential to redefine out-of-school AI education to be intersectional based on Black girls’ perspectives, values, and knowledge.

Findings will be synthesized to inform an understanding of how curricula and integrated technologies, and facilitator professional development can be aligned to cultivate an intersectional AI learning ecosystem. The researchers will advance AI educators’ pedagogical skills through facilitator professional development to (1) operationalize intersectional principles in the design of AI curricula and integrated technologies, (2) utilize a co-design framework for AI curricula and integrated technologies and (3) unlearn the dominant norms embedded in traditional systems of AI education through culturally responsive computing practices. They will freely publish curricula and professional development resources for future organizations to develop their intersectional AI learning ecosystem. At the project’s completion, Nkrumah and her team will have developed two sustainable programs for Black girls which can continuously support AI education.

Note: The project has a strict non-discrimination policy, and all individuals are able to apply and participate in programs and events without regard to race, sex, gender identification, sexual orientation, national origin, native language, religion, age, disability, marital status, citizenship, genetic information, pregnancy or any other characteristic protected by law.