Black Life Within K–12 Artificial Intelligence and Machine Learning Education (Poster 7)

• In Event: Perspectives on Promoting Algorithmic Justice in Artificial Intelligence and Machine Learning Education in K–12

Abstract

Objectives or purposes: I meditate on what artificial intelligence and machine learning (AIML) mean for the future of Black Life. What of Black Life, when facial recognition surveillance systems misidentify Black folks, and they are falsely arrested (NBCnews, 2023)? What of Black Life, when gang databases are populated with infants (NBCnews, 2016)? What of Black Life, when prolific scholars are fired for speaking caution in our approaches to AIML (Metz, C., & Wakabayashi, D., 2020)? As Katherine McKittrick has asked, is there an algorithm that can hold Black Life (McKittrick, 2020)? In this poster, I take up these questions and interrogate the relationships between AIML and Black Life. I am broadly interested in thinking about the role of AIML in replicating and producing Black death, and the potential use of AIML for learning about Black Life.
Theoretical framework: I draw from BlackCrit (Dumas & ross, 2016) to examine how anti-Blackness within AIML spaces is a permeating topic. BlackCrit asks us to interrogate how learning spaces are sites that replicate anti-Black ideologies. anti-Blackness is expansive and fungible, and involves ever-changing strategies for the dehumanization of Black people. Within computer science education, scholars have highlighted how anti-Blackness is present and might be addressed through abolitionist praxis (Jones & melo, 2021). Within this poster, I explore how we might think about the stakes of learning for K-12 Children within contexts of anti-Blackness.
Methods and Data Sources: In this poster, I utilize speculative fiction methods to offer four short vignettes that focus on different aspects of AIML and K-12 student experiences with learning them. These are then used to situate example reactions, feelings, or learning that happens within the scenarios. Utilizing BlackCrit I attend to how anti-Blackness shows up in these vignettes, and ways that Black Life might be honored within the vignettes.
Results: The first vignette ties in concepts and ethics of generative AIML. The second vignette considers how race has been treated as a technology (Benjamin, 2019). The third vignette highlights tensions of surveillance within AIML systems. The fourth vignette offers an example of young people building out the next generation of AIML systems. This series of vignettes show a glimpse of the different intersections of anti-Blackness and Black Life within AIML learning spaces.
Significance: This poster offers an opportunity for researcher-educators to reflect on how anti-Blackness plays a role in AIML systems. Further, this provides an opportunity for us to contemplate the effects of anti-Black racism on AIML learning experiences, and create pathways forward for more Algorithmic Justice. I push us to examine our practices not with an eye towards failure but rather as a moment for new beginnings in strategically building more just futures.

Author

• Stephanie T. Jones, Northwestern University