The increasing deployment of artificial intelligence (AI) tools to inform decision making across diverse areas including healthcare, employment, social benefits, and government policy, presents a serious risk for disabled people, who have been shown to face bias in AI implementations. While there has been significant work on analysing and mitigating algorithmic bias, the broader mechanisms of how bias emerges in AI applications are not well understood, hampering efforts to address bias where it begins. In this article, we illustrate how bias in AI-assisted decision making can arise from a range of specific design decisions, each of which may seem self-contained and non-biasing when considered separately. These design decisions include basic problem formulation, the data chosen for analysis, the use the AI technology is put to, and operational design elements in addition to the core algorithmic design. We draw on three historical models of disability common to different decision-making settings to demonstrate how differences in the definition of disability can lead to highly distinct decisions on each of these aspects of design, leading in turn to AI technologies with a variety of biases and downstream effects. We further show that the potential harms arising from inappropriate definitions of disability in fundamental design stages are further amplified by a lack of transparency and disabled participation throughout the AI design process. Our analysis provides a framework for critically examining AI technologies in decision-making contexts and guiding the development of a design praxis for disability-related AI analytics. We put forth this article to provide key questions to facilitate disability-led design and participatory development to produce more fair and equitable AI technologies in disability-related contexts.

In this paper, we study the problem of identifying logos of business brands in natural scenes in an open-set one-shot setting. This problem setup is significantly more challenging than traditionally-studied 'closed-set' and 'large-scale training samples per category' logo recognition settings. We propose a novel multi-view textual-visual encoding framework that encodes text appearing in the logos as well as the graphical design of the logos to learn robust contrastive representations. These representations are jointly learned for multiple views of logos over a batch and thereby they generalize well to unseen logos. We evaluate our proposed framework for cropped logo verification, cropped logo identification, and end-to-end logo identification in natural scene tasks; and compare it against state-of-the-art methods. Further, the literature lacks a 'very-large-scale' collection of reference logo images that can facilitate the study of one-hundred thousand-scale logo identification. To fill this gap in the literature, we introduce Wikidata Reference Logo Dataset (WiRLD), containing logos for 100K business brands harvested from Wikidata. Our proposed framework that achieves an area under the ROC curve of 91.3% on the QMUL-OpenLogo dataset for the verification task, outperforms state-of-the-art methods by 9.1% and 2.6% on the one-shot logo identification task on the Toplogos-10 and the FlickrLogos32 datasets, respectively. Further, we show that our method is more stable compared to other baselines even when the number of candidate logos is on a 100K scale.

Recent work has shown the potential of graph neural networks to efficiently predict material properties, enabling high-throughput screening of materials. Training these models, however, often requires large quantities of labelled data, obtained via costly methods such as ab initio calculations or experimental evaluation. By leveraging a series of material-specific transformations, we introduce CrystalCLR, a framework for constrastive learning of representations with crystal graph neural networks. With the addition of a novel loss function, our framework is able to learn representations competitive with engineered fingerprinting methods. We also demonstrate that via model finetuning, contrastive pretraining can improve the performance of graph neural networks for prediction of material properties and significantly outperform traditional ML models that use engineered fingerprints. Lastly, we observe that CrystalCLR produces material representations that form clusters by compound class.

Background The use of a clinical decision support system for assessing the quality of care, based on computerized clinical guidelines (GLs), is likely to improve care, reduce costs, save time, and enhance the staff's capabilities. Objectives Implement and evaluate a system for assessment of the quality of the care, in the domain of management of pressure ulcers, by investigating the level of compliance of the staff to the GLs. Methods Using data for 100 random patients from the local EMR system we performed a technical evaluation, checking the applicability and usability, followed by a functional evaluation of the system investigating the quality metrics given to the compliance of the medical's staff to the protocol. We compared the scores given by the nurse when supported by the system, to the scores given by the nurse without the system's support, and to the scores given by the system. We also measured the time taken to perform the assessment with and without the system's support. Results There were no significant differences in the scores of most measures given by the nurse using the system, compared to the scores given by the system. There were also no significant differences across the values of most quality measures given by the nurse without support compared to the values given by the nurse with support. Using the system, however, significantly reduced the nurse's average assessment time. Conclusions Using an automated quality-assessment system, may enable a senior nurse, to quickly and accurately assess the quality of care. In addition to its accuracy, the system considerably reduces the time taken to assess the various quality measures.

Android malware attacks are increasing daily at a tremendous volume, making Android users more vulnerable to cyber-attacks. Researchers have developed many machine learning (ML)/ deep learning (DL) techniques to detect and mitigate android malware attacks. However, due to technological advancement, there is a rise in android mobile devices. Furthermore, the devices are geographically dispersed, resulting in distributed data. In such scenario, traditional ML/DL techniques are infeasible since all of these approaches require the data to be kept in a central system; this may provide a problem for user privacy because of the massive proliferation of Android mobile devices; putting the data in a central system creates an overhead. Also, the traditional ML/DL-based android malware classification techniques are not scalable. Researchers have proposed federated learning (FL) based android malware classification system to solve the privacy preservation and scalability with high classification performance. In traditional FL, Federated Averaging (FedAvg) is utilized to construct the global model at each round by merging all of the local models obtained from all of the customers that participated in the FL. However, the conventional FedAvg has a disadvantage: if one poor-performing local model is included in global model development for each round, it may result in an under-performing global model. Because FedAvg favors all local models equally when averaging. To address this issue, our main objective in this work is to design a dynamic weighted federated averaging (DW-FedAvg) strategy in which the weights for each local model are automatically updated based on their performance at the client. The DW-FedAvg is evaluated using four popular benchmark datasets, Melgenome, Drebin, Kronodroid and Tuandromd used in android malware classification research.

An executive of Baidu Inc., the Chinese search-engine giant and major artificial-intelligence company, shrugged off new U.S. export restrictions on advanced semiconductors designed to slow China's military advance. Baidu's Executive Vice President Dou Shen said in an earnings call with analysts on Tuesday the U.S. export controls would have limited short-term impact on the company, and he believes its AI businesses would benefit from the new rules in the long run. In October, the U.S. Commerce Department rolled out new restrictions on advanced chip technology, which require a license for U.S. companies to export cutting-edge chips used for AI and supercomputing and chip-making equipment key to China's technological goals. The move vastly expanded on existing rules restricting the export of advanced technologies to China. Mr. Shen said that a large portion of Baidu's AI and cloud-computing businesses don't rely heavily on advanced chips and that the Beijing-based company had stocked enough high-end chips for its businesses that need them.

The composition of the academic workforce thus shapes what advances are made and who benefits from them,20,21 in part because demographic diversity in science is known to accelerate innovation and improve problem solving.17,31 Despite a continued emphasis on broadening participation, women faculty in the U.S. remain underrepresented relative to women's share of the U.S. population by more than a factor of two, and Black, Hispanic, and Native faculty by more than a factor of five.37,40 Women's underrepresentation among computing researchers also persists internationally. For example, women are estimated to comprise less than 10% of contributors to international computer science journals.25 On one hand, there are generational problems, in which faculty diversity changes slowly because it takes many years for diversity increases at the earliest stages of training to propagate up to more senior levels.16 On the other hand, there are structural and social climate problems in the U.S.,1 in which members of underrepresented groups who aspire to or have a faculty career are pushed or pulled out of the community, which may counteract efforts to address generational problems. In concert, these two effects may lead to a persistent overrepresentation of majority groups5 despite efforts to the contrary. We consider a third class of problem, which exists because most faculty are hired via searches that focus on a particular subfield of computing--for example, artificial intelligence (AI). As a result, field-level demographic dynamics such as gender, racial, and socioeconomic representation are in fact driven by diversity differences across computing's subfields and the representation of those subfields among the suppliers of future faculty.8 For example, faculty searches in subfields with fewer women than other subfields are less likely to increase a department's gender diversity.

During the last 30 years, digital data and artificial intelligence (AI) to exploit that data have emerged as central to management of our society. At the same time, the development of digital networks and big computing centers has promoted centralization of data and digital systems, leaving individuals and communities outside of this new digital ecosystem and without the ability to control local finance, health, or governance systems. New distributed technologies, loosely described as Web3 and employing technologies such as federated AI, blockchain, Internet of Things (IoT), and others, have the potential to give back control of data, AI, and its benefits to individuals and communities. In addition to the many private efforts now being launched, some national governments are aggressively pursuing this new suite of technologies, but with much stronger government oversight. Consequently, there is an urgent need to develop standards that guarantee a Web3 economy that remains truly distributed and yet provides global interoperability along with adequate protection for individuals and communities.

Despite the many wondrous uses of artificial intelligence (AI), examples of "fragile" AI are increasingly common. Facial recognition systems make serious errors when presented With images of people of color. Large-language models spew out fluent text that on closer inspection makes little sense. Adversaries successfully confuse AI systems by corrupting sensor data. Automatic missile defense systems have sometimes mistaken commercial aircraft for enemy warplanes.

The U.S. Veterans Health Administration developed a suicide prediction statistical model and implemented a novel clinical program, Recovery Engagement and Coordination for Health–Veterans Enhanced Treatment (REACH VET). This high-value suicide prevention program aims to efficiently identify patients at risk and connect them with care. Starting in April 2017, national REACH VET metric data were collected from electronic health records to evaluate required task completion. By October 2020, 98% of veterans identified (N=6,579) were contacted by providers and had their care evaluated. In the nation’s largest health care system, it was feasible to implement a clinical program based on a suicide prediction model.