While much research has shown the presence of AI’s “under-the-hood” biases (e.g., algorithmic, training data, etc.), what about “over-the-hood” inclusivity biases: barriers in user-facing AI products that disproportionately exclude users with certain problem-solving approaches? Recent research has begun to report the existence of such biases—but what do they look like, how prevalent are they, and how can developers find and fix them? To find out, we conducted a field study with 3 AI product teams, to investigate what kinds of AI inclusivity bugs exist uniquely in user-facing AI products, and whether/how AI product teams might harness an existing (non-AI-oriented) inclusive design method to find and fix them. The teams’ work revealed 83 instances of 6 AI inclusivity bug types unique to user-facing AI products, their fixes covering 47 bug instances, and a new GenderMag inclusive design method variant, GenderMag-for-AI, that is especially effective at detecting AI inclusivity bugs when the AI’s output is not necessarily believed.

Abstract Motivations . Explainable AI (XAI) systems aim to improve users’ understanding of AI, but XAI research has shown that many XAI explanations serve some users well while failing others. In non-AI systems, software practitioners have used inclusive design approaches to address similar problems, sometimes creating “curb-cut” improvements that benefit both underserved users and everyone else. This raises the possibility that inclusive design approaches can bring similar curb-cut improvements to AI explanations. Objectives . Our objective was to investigate possible curb-cut effects of inclusivity-driven fixes an AI product team made using an inclusive design approach (GenderMag) to improve their XAI prototype. Methods . We ran a between-subject study with 69 participants who had no formal AI background. 34 participants used the original version of the XAI prototype and the rest used the version with the AI team’s inclusivity fixes. We then compared the two groups’ mental model concepts scores and prediction accuracy, and the two prototypes’ inclusivity. Results . Our investigation produced four main results. First, the AI team’s inclusivity fixes were overall effective, resulting in overall better conceptual mental models with the new prototype. Further (second), the AI team’s inclusivity fixes were particularly beneficial to the underserved population’s conceptual mental models—which, together with the first result, constitutes a curb-cut effect. However (third), the inclusivity fixes did not improve participants’ prediction accuracy scores. Instead, it appears to have harmed them overall—a “curb-fence” effect (opposite of a curb-cut effect). Finally (fourth), the AI team’s fixes improved equity, reducing the gender gap by 45%.

Educational perspectives are largely absent in explainable artificial intelligence (XAI) design, especially outside the education domain. Designing explanations without educational perspectives can reduce explanations to mere informational outputs, which can limit user engagement and understanding. Instead, I hypothesize XAI can become more effective if treated as a type of education that can be used for educating users about AI’s decision-making processes. To explore this overall hypothesis, I plan to-1) conduct a literature review of educational theories and practices that can be applied for modifying an existing XAI system, 2) propose a set of hypotheses for testing the effectiveness of education-driven design changes in improving XAI, 3) apply educational theories and practices to modify an existing XAI system outside the education domain, and 4) conduct iterative qualitative evaluation with users and education specialists and then a quantitative lab study for testing the hypotheses to prove that education-driven design changes can improve XAI.

Intersectional HCI recognizes that humans' interconnected social identities shape their experiences with technology. However, intersectional HCI requires extensive resources, such as access to intersectional populations, which many HCI practitioners may lack. For these practitioners, we present an analytical approach to bring intersectional lenses to HCI practices. The approach uses types—not at the level of identities, but at the level of personal traits drawn from foundational research. We first formally prove that certain analytical methods for detecting inclusivity issues can be meaningfully composed to provide equitable consideration of typically overlooked populations; then present four design use-cases to illustrate what the approach brings to HCI practices; and then empirically investigated one of the four use-cases with 24 HCI participants. Results show that practitioners using the compositional approach detected even more intersectional inclusivity problems than those using a complementary intersectional approach.

In this paper, we present DendroMap, a novel approach to interactively exploring large-scale image datasets for machine learning (ML). ML practitioners often explore image datasets by generating a grid of images or projecting high-dimensional representations of images into 2-D using dimensionality reduction techniques (e.g., t-SNE). However, neither approach effectively scales to large datasets because images are ineffectively organized and interactions are insufficiently supported. To address these challenges, we develop DendroMap by adapting Treemaps, a well-known visualization technique. DendroMap effectively organizes images by extracting hierarchical cluster structures from high-dimensional representations of images. It enables users to make sense of the overall distributions of datasets and interactively zoom into specific areas of interests at multiple levels of abstraction. Our case studies with widely-used image datasets for deep learning demonstrate that users can discover insights about datasets and trained models by examining the diversity of images, identifying underperforming subgroups, and analyzing classification errors. We conducted a user study that evaluates the effectiveness of DendroMap in grouping and searching tasks by comparing it with a gridified version of t-SNE and found that participants preferred DendroMap. DendroMap is available at https://div-lab.github.io/dendromap/.

In a developing country like Bangladesh, minority ethnic groups or tribal people are less privileged to use the benefit of ICT interventions. The smartphone applications hardly persuade the tribal communities. Even though smart cell phones are cheaply available to these people, there is hardly any significant impact of mobile applications. We have studied the usage of smartphone and its applications by the people of various tribal communities. Especially, the UX/UI of the two popular smartphone applications (bKash and Bikroy.com) has been evaluated according to the usage of tribal people. Interestingly, we have found that the culture, language, and customs of the ethnic minority groups make issues to have a successful interaction with those two applications. The local software industries never consider them as a stakeholder of the generic software during the developments. In this paper, we not only have addressed the challenges and issues regarding UX/UI of smart applications for tribal people but also a suitable solution has been recommended in terms of design and user experience of tribal people. The findings of this paper can help in developing mobile applications and services which will be beneficent to the tribal and ethnic people.