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Historically, the African American female population has been underrepresented within the STEM workforce, creating non-inclusive environments. The metaphor of getting a seat at the table reflects the idea of inclusivity where Black women’s opinions are valued, and they can make decisions and create change within the workplace. Intersectionality poses several unique challenges for Black women pursuing careers in the video games industry. We motivate further research on this group through existing literature and insights from running a research group on the group design of human-centered data science games in a collaborative university setting. We take an autoethnographic perspective on the topic, with the first and second authors grounding their findings in their own experiences as Black women in STEM and higher education and with video game development studios. We outline tangible actions toward the recruitment, development, and retention of Black women in the video games industry in the future. 

From our smartphones to our social media, artificial intelligence (AI) algorithms are becoming ubiquitous in our everyday lives. However, the conveniences that they bring come alongside many potential social and political harms. It is imperative that members of the public develop data ethics literacy to interpret AI’s harms and benefits daily. The immersive and transformative nature of games may enable a wide range of people to explore complex ethical concepts in AI and data science through the lens of speculative design. In this project, we focus on the learning process of a diverse group of students from two universities as they embark upon a process of game design to teach ethical thinking in data science/AI. Through qualitative analysis of semi-structured interviews, we apply a speculative game design framework to identify aspects that aid student learning. 

Data science has become an important topic for the CHI conference and community, as shown by many papers and a series of workshops. Previous workshops have taken a critical view of data science from an HCI perspective, working toward a more human–centered treatment of the work of data science and the people who perform the many activities of data science. However, those approaches have not thoroughly examined their own grounds of criticism. In this workshop, we deepen that critical view by turning a reflective lens on the HCI work itself that addresses data science. We invite new perspectives from the diverse research and practice traditions in the broader CHI community, and we hope to co-create a new research agenda that addresses both data science and human-centered approaches to data science.