Media representations of people with disabilities profoundly influence societal perceptions, yet have historically been absent, stereotyped, or inaccurate. As AI-generated visual media becomes increasingly prevalent, there is a critical opportunity to address these misrepresentations. Responding to the lack of collectively negotiated representation standards, this paper presents our human-centric approach to engaging disability communities meaningfully in AI data practices. Over three months, we worked closely with three disability organizations across the Global North and South to develop the Community Library Creator that introduces design scaffolds to support communities in defining ‘good’ representation and curating community-centric AI datasets; laying the foundations for community-specific evaluation metrics and future model adaptations. We contribute qualitative insights into the complexities of community-led data curation; discuss the value and practical challenges of intersecting human insights with AI requirements; and reflect on human-centered AI approaches that empower communities to share their perspectives and actively shape AI data practices.

Despite the effectiveness of adversarial training (AT) in enhancing model robustness, it suffers from the accuracy-robustness trade-off and the “robust fairness” problem. To strategize effectively, practitioners have the need to explore and compare model performance in both standard and adversarial settings concurrently. This work presents a design study with 11 experts to explore effective comparative visual techniques for multi-level trade-off analysis. We first collaborated with five adversarial machine learning (AML) experts in an iterative design process, based on which we developed a visual analytics design probe, VATRA, that employs an augmented hybrid comparative design to support concurrent accuracy and robustness evaluations for assessing model trade-offs. Further, we conducted user studies with six domain experts and derived two in-depth use cases of VATRA, providing empirical knowledge about how ML practitioners can leverage comparative visualizations for AML trade-off analysis.

Adversarial machine learning (AML) studies attacks that can fool machine learning algorithms into generating incorrect outcomes as well as the defenses against worst-case attacks to strengthen model robustness. Specifically for image classification, it is challenging to understand adversarial attacks due to their use of subtle perturbations that are not human-interpretable, as well as the variability of attack impacts influenced by diverse methodologies, instance differences, and model architectures. Through a design study with AML learners, and teachers, we introduce AdvEx , a multi-level interactive visualization system that comprehensively presents the properties and impacts of evasion attacks on different image classifiers for novice AML learners. We quantitatively and qualitatively assessed AdvEx in a two-part evaluation including user studies and expert interviews. Our results show that AdvEx is not only highly effective as a visualization tool for understanding AML mechanisms but also provides an engaging and enjoyable learning experience, thus demonstrating its overall benefits for AML learners.

Natural language (NL) programming has become more approachable due to the powerful code-generation capability of large language models (LLMs). This shift to using NL to program enhances collaborative programming by reducing communication barriers and context-switching among programmers from varying backgrounds. However, programmers may face challenges during prompt engineering in a collaborative setting as they need to actively keep aware of their collaborators’ progress and intents. In this paper, we aim to investigate ways to assist programmers’ prompt engineering in a collaborative context. We first conducted a formative study to understand the workflows and challenges of programmers when using NL for collaborative programming. Based on our findings, we implemented a prototype, CoPrompt, to support collaborative prompt engineering by providing referring, requesting, sharing, and linking mechanisms. Our user study indicates that CoPrompt assists programmers in comprehending collaborators’ prompts and building on their collaborators’ work, reducing repetitive updates and communication costs.