The KeepA(n)I platform facilitates the auditing of computer vision systems that tag images, which aid visual communication on the Web and social media, from content moderation to the development of new apps and tools. In particular, KeepA(n)I enables a broad set of stakeholders to scrutinize a process of interest that embeds an image tagger for issues of social stereotyping, while also examining the social norms that humans apply to the observed AI behaviors. KeepA(n)I’s approach, and its use of the power of the crowd, can aid the stakeholders in receiving responses to both descriptive and normative questions (i.e., which stereotyping behaviors are observed and if they are considered problematic by a given “crowd” for an intended context). We provide an overview of the platform, its key features, and a discussion via a use case on the diverse set of stakeholders that can benefit from it.
Autonomous driving relies on computationally intensive perception pipelines to continuously detect and track objects in the surrounding environment. While some objects are key to plan safe and effective maneuvers, others may not be relevant and have no impact on the autonomous ve…
У статті досліджуються епістемологічні та алгоритмічні обмеження сучасних моделей комп'ютерного зору (зокрема архітектури YOLOv8) щодо автоматизованого розпізнавання складних психоемоційних станів в умовах авіаційної інфраструктури. На основі трансдисциплінарної методології…
Echocardiographic assessment of End-Diastolic Volume (EDV) is central to identifying dilated cardiomyopathy, a major driver of heart failure. This paper presents a low-latency, edge-computing solution that deploys an 8-bit quantized, 50%-pruned CNN directly onto a Xilinx Artix-7…
Accurate delineation of coronary arteries from X-ray angiography supports early identification of narrowing or blockages, but is complicated by low contrast and thin, branching vessel structures. This study compares three U-Net-based segmentation architectures — U-Net, U-Ne…
Voice characteristics are an emerging, non-invasive biomarker for heart failure. This study develops a machine learning pipeline to differentiate patients with suspected heart failure from those with a confirmed diagnosis using vocal features alone, drawing on 240 patients (50 su…