This paper analyzes where Artificial Intelligence (AI) ethics research fails and breaks down the dangers of well-intentioned but ultimately performative ethics research. A large majority of AI ethics research is criticized for not providing a comprehensive analysis of how AI is interconnected with sociological systems of oppression and power. Our work contributes to the handful of research that presents intersectional, Western systems of oppression and power as a framework for examining AI ethics work and the complexities of building less harmful technology; directly connecting technology to named systems such as capitalism and classism, colonialism, racism and white supremacy, patriarchy, and ableism. We then explore current AI ethics rhetoric’s effect on the AI ethics domain. We conclude by providing an applied example to contextualize intersectional systems of oppression and AI interventions in the US justice system and present actionable steps for AI practitioners to participate in a less performative, critical analysis of AI. This article appears in the AI & Society track.

Historically, female students have shown low interest in the field of computer science. Previous computer science curricula have failed to address the lack of female-centered computer science activities, such as socially relevant and real-life applications. Our new summer camp curriculum introduces the topics of artificial intelligence (AI), machine learning (ML) and other real-world subjects to engage high school girls in computing by connecting lessons to relevant and cutting edge technologies. Topics range from social media bots, sentiment of natural language in different media, and the role of AI in criminal justice, and focus on programming activities in the NetsBlox and Python programming languages. Summer camp teachers were prepared in a week-long pedagogy and peer-teaching centered professional development program where they concurrently learned and practiced teaching the curriculum to one another. Then, pairs of teachers led students in learning through hands-on AI and ML activities in a half-day, two-week summer camp. In this paper, we discuss the curriculum development and implementation, as well as survey feedback from both teachers and students.

The Computer Science Frontiers (CSF) project introduces teachers to the topics of artificial intelligence and distributed computing to engage their female students in computing by connecting lessons to relevant cutting edge technologies. Application topics include social media and news articles, as well as climate change, the arts (movies, music, and museum collections), and public health/medicine. CSF educators are prepared in a pedagogy and peer-teaching centered professional development program where they simultaneously learn and teach distributed computing, artificial intelligence, and internet of things lessons to each other. These professional developments allow educators to hone in on their teaching skills of these new topics and gain confidence in their ability to teach new computer science materials before running several activities with their students in the academic year classroom. In this workshop, teachers participating in the CS Frontiers professional development will give testimonials discussing their experiences teaching these topics in a two week summer camp. Attendees will then try out three computing activities, one from each Computer Science Frontiers module. Finally, there will be a question and answer session.

Nationwide attention towards the Black Lives Matter (BLM) movement has motivated many individuals to seek resources related to racial equity activism education. Heightened awareness of racially motivated police brutality has highlighted the realities of systemic racism in the United States' institutions and policies. It has also spurred a growing interest in online platforms offering relevant educational resources, thus demonstrating a need for applications specializing in education around BLM and its platform of anti-racism, police brutality, and equity. This research's purpose is to develop a tool that specializes in directing users who may be unsure how to begin educating themselves to relevant resources. We developed ANTIE, the activism chatbot, using the IBM Watson Assistant framework due to its capability of simulating human-like conversations. We further increased the potential for high levels of user engagement by including fun language and emojis. Overall, our project presents ANTIE in beta. The chatbot currently promotes BLM education by providing voting information, relevant news articles, terminology definitions, media recommendations, a directory of local Black-owned businesses, and other resources. We conducted pilot testing with six participants; however, we plan to gather more results via deployment to a larger audience. Our next steps are to evaluate and improve ANTIE by surveying users' levels of engagement and knowledge about racial equity activism after interacting with the chatbot.

News articles can have a profound influence on voter opinions and preferences according to studies conducted on the effects of bias in political news coverage. Because political opinions and preferences can be persuaded, it is important that readers are aware of biases embedded within a story, as it may be perceived differently by others and motivate readers to find different sources to inform their opinions. Whereas previous research has attempted to characterize bias strictly from article text, ours examines the subjective perception of it as a function of the reader's political leanings and salience of the article's source. Our research seeks to determine if we can empirically verify in-group out-group bias in news sources rather than news article content. We present Mechanical Turk participants with a neutral definition of bias and administer a survey that collects data on whether or not readers believe the given definition of bias applies to the given article. The three news sources (CNN, Fox News, and BBC) are randomly displayed as the authoring body on the same article content. Afterward, participants answer demographic questions including their own political leanings. Furthermore, we will discuss the perceived bias by political camp, implications for news source bias, and our goal to harness the results of this experiment in pursuit of training machine learning models that can detect bias from the perspectives of members belonging to particular political leanings.

This paper illustrates coding decisions by in-service teachers and high school interns working independently versus collaboratively to build computing activities for non-computing classrooms. We investigate code written in Snap! to gain insights on project type and subject matter. We also share case studies on how intern collaboration influences final product execution. Through our research, we found student-only teams often created tutorial projects whereas teachers-only teams create interactive narratives. We found students were able to reuse code across projects to replicate similar mechanics and that students specialize in different aspects of project creation. Overall, we find it beneficial to have collaborative teacher-student teams.