The relentless pace of video production exacerbates the digital accessibility gap that individuals who are blind or low vision (BLV) face on a daily basis, resulting in disproportionate exclusion from community opportunities and risk management. Whereas previous automated audio description (AD) systems provide single-tool approaches for delivering minimum viable description (MVD) or delivering on-demand visual question answering (VQA), we present a tandem AI-based AD tool that combines MVD and on-demand VQA. A user study with 26 BLV individuals explored how the tandem system may be used under the conditions of delivering MVD and/or on-demand VQA with AI-only or human-in-the-loop support. When each tool was used in isolation, AI-only conditions scored significantly lower in both user enjoyment and comprehension. When used in tandem, AI-only conditions matched outcomes delivered with human-in-the-loop, which suggests that AI-only AD tools may be most effective when both types of tools are used in tandem. A multimodal analysis of interactions with the tandem system revealed areas for system improvement in terms of the timing of AD delivery and accurate content delivery. We discuss how the use of both types of tools in a tandem system can mitigate some of the digital frictions that have plagued efforts in machine learning and automated tools for accessibility.

We present a new high-quality validated image caption rating (VICR) dataset. How well a caption fits an image can be difficult to assess due to the subjective nature of caption quality. How do we evaluate whether a caption is good? We generated a new dataset to help answer this question by using our new image caption rating system, which consists of a novel robust rating scale and gamified approach to gathering human ratings. We show that our approach is consistent and teachable. 113 participants were involved in generating the dataset, which is composed of 68,217 ratings among 15,646 image-caption pairs. Our new dataset has greater inter-rater agreement than the state of the art, and custom machine learning rating predictors that were trained on our dataset outperform previous metrics. We improve over Flickr8k-Expert in Kendall's $W$ by 12\% and in Fleiss' $\kappa$ by 19\%, and thus provide a new benchmark dataset for image caption rating.

Science students increasingly need programming and data science skills to be competitive in the modern workforce. However, at our university (San Francisco State University), until recently, almost no biology, biochemistry, and chemistry students (from here bio/chem students) completed a minor in computer science. To change this, a new minor in computing applications, which is informally known as the Promoting Inclusivity in Computing (PINC) minor, was established in 2016. Here, we present the lessons we learned from our experience in a set of 10 rules. The first 3 rules focus on setting up the program so that it interests students in biology, chemistry, and biochemistry. Rules 4 through 8 focus on how the classes of the program are taught to make them interesting for our students and to provide the students with the support they need. The last 2 rules are about what happens "behind the scenes" of running a program with many people from several departments involved.

This qualitative analysis explores the development of Computer Science (CS) identity of university students majoring in CS and students participating in a novel CS minor geared toward students who have traditionally been underrepresented in the field. We examine student perceptions of their CS identity at two critical junctures: pre-CS exposure (initial interest in CS), and during early CS exposure (performance and competence). Findings demonstrate the different paths to CS identity that the groups take, and highlight the importance of the CS educational environment in efforts to diversify the field.

Underrepresentation of women and Black and Latinx individuals in computer science (CS) is a well-documented issue facing university training programs and the field in general. In an effort to expand on current knowledge and help bridge the equality gap in CS, a pilot interdisciplinary Computing Applications minor program was started in 2016 at San Francisco State University (SFSU). The "Promoting INclusivity in Computing" (PINC) program was designed to improve diversity in computing and increase computing literacy in data-intensive fields, specifically biology and chemistry. Students participating in the PINC program were compared to lower division CS majors at SFSU on measures assessing their attitudes toward computer science, goal-setting tendencies, experience of stereotype threat, general self-efficacy, and computer science self-efficacy. Analysis showed that students in the PINC program reported lower levels of computer science self-efficacy, but there were no significant differences between groups on self-report measures of goal-setting tendencies, experience of stereotype threat, attitudes toward computer science, and general self-efficacy. These findings highlight the success of the minor program in creating an educational environment that supports the achievement of underrepresented (UR) students, as well as the similar psychoeducational traits of the two groups of students. Findings may be of particular interest to postsecondary CS teachers, researchers interested in social justice and representation issues as they pertain to the field of computing, and university and departmental administrators who wish to increase and promote diversity in their CS programs.