AI is increasingly framed as a solution to societal challenges, particularly educational inequities. Yet, AI systems often reproduce the very exclusionary structures they claim to address. Despite this, there is limited research on empowering minoritized youth to shape AI in ways that align with their communities’ needs. This systemic issue is especially significant for Child–Computer Interaction research, which must examine how young people critically engage with AI technologies. Drawing on data science projects, surveys, and semi-structured interviews, we document how youth develop anti-AI solutionist perspectives alongside data science practices, and how they re-imagine more justice-oriented roles for AI. Our primary contribution shows how our critical AI fluency curricula enable youth to determine when AI should be adopted or refused because they understand racial inequities as sociotechnical rather than technological problems. We position this capacity for AI refusal as essential for redistributing power under algorithmic governance.

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of six AI assignments from the 2023 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu .

ADM systems can be used to perform a task as inconsequential as recommending a song on Spotify, to making a decision that is instrumental to someone's life, such as determining their candidacy for college. If an algorithm is trained on biased data, it can propagate prejudice. Thus, it is pertinent to find methods to decrease ADM bias. This paper presents a way to potentially mitigate ADM bias by teaching high school students a intersectional data analysis activity that incorporates the second pillar of the liberatory computing framework, critical consciousness. This activity is designed to enable high school students to understand the bias and history behind the college admission process, which allows students to develop a critical consciousness. Establishing a critical consciousness will diversify the computing field and the data incorporated into ADM systems by encouraging minoritized high school students to get a degree in computer science. The National Institute of Standards and Technology (NIST) suggests that diversifying the computing field has the potential to reduce bias in ADM systems. Thus, the activity is focused on students developing a critical consciousness. This paper discusses the preliminary findings from teaching a two-day computing activity to high school students.

There is a small percentage of African Americans in the computing field because they lack opportunities to participate in data science programs, particularly in ways that empower their community. Current computing curricula do not teach students how to leverage their technical skills in service of projects that are more authentic and relevant to African American students. I introduce the first high school data activism curriculum, which contains a systemic justice capstone project. The capstone emphasizes the importance of understanding the history and intersectionality of a unique problem just as much as the rigorous data science skills to create prolonged change. My thematic analysis shows that the students were able to apply their data science and social justice skills to a project that mitigates systemic oppression.

Biased data is increasingly becoming a part of algorithms that deter- mine people's livelihood, such as predictive policing or recidivism predictors. One of the most effective ways of understanding how such algorithms work starts by examining the systems of oppression that lead to biased data. The lesson, "Intersectional Data Analysis: Examining Shootings in Boston", begins with examining the connection between racism, housing, and policing. Then, students use their data science skills to analyze how gun violence disproportionately harms African Americans. As a result, students examine the direct effects of historical bias embedded in data. The results show the student's ability to use data science and their knowledge of gun violence being a racial justice issue to create unbiased datasets, which may lead to fair algorithms.

As social robots rapidly become mainstream technologies, it is critical for HRI researchers and practitioners to consider their societal and ethical impacts as well as their ability to perpetuate or mitigate intersectional social inequities and hierarchies relating to race, class, gender, disability, and other social axes. Through an equity, ethics, and justice-centered audit of human-robot interaction (HRI) scholarship, we reveal how the HRI community has engaged with these topics over the past two decades. We use the five senses ethical framework that has been proposed specifically for use in HRI contexts to perform the review paired with an analysis of equity and justice. We then expand the Design Justice framework (a framework for analyzing how design impacts society and distributes benefits and burdens to society through the lenses of equity, values, scope, ownership, and accountability) to HRI contexts through the inclusion of HRI-specific topics such as autonomy, transparency, deception, and policies. We invite researchers and practitioners to explore the HRI Equitable Design framework to work towards designing equitable and inclusive HRI research studies and technologies.