This matching requires researchers to know the exact wording that other researchers previously used, creating a challenging process that could lead to missing relevant data. Large Language Models (LLMs) could enhance data discovery by removing this requirement and allowing researchers to ask questions with natural language. However, we do not currently know if researchers would accept LLMs for data discovery. Using a human-centered artificial intelligence (HCAI) focus, we ran focus groups (N = 27) to understand researchers' perspectives towards LLMs for data discovery. Our conceptual model shows that the potential benefits are not enough for researchers to use LLMs instead of current technology. Barriers prevent researchers from fully accepting LLMs, but features around transparency could overcome them. Using our model will allow developers to incorporate features that result in an increased acceptance of LLMs for data discovery.

The rapid growth of artificial intelligence (AI) technologies has changed decision-making in many fields. But, it has also raised major privacy and ethical concerns. However, many AI incidents taxonomies and guidelines for academia, industry, and government lack grounding in real-world incidents. We analyzed 202 real-world AI privacy and ethical incidents. This produced a taxonomy that classifies incident types across AI lifecycle stages. It accounts for contextual factors such as causes, responsible entities, disclosure sources, and impacts. Our findings show insufficient incident reporting from AI developers and users. Many incidents are caused by poor organizational decisions and legal non-compliance. Only a few legal actions and corrective measures exist, while risk-mitigation efforts are limited. Our taxonomy contributes a structured approach in reporting of future AI incidents. Our findings demonstrate that current AI governance frameworks are inadequate. We urgently need child-specific protections and AI policies on social media. They must moderate and reduce the spread of harmful AI-generated content. Our research provides insights for policymakers and practitioners, which lets them design ethical AI. It also support AI incident detection and risk management. Finally, it guides AI policy development. Improved policies will protect people from harmful AI applications and support innovation in AI systems.

Expanding the involvement of women in Science, Technology, Engineering, and Mathematics (STEM) across Latin America is crucial for economic advancement, social equity, and global competitiveness; however, these efforts have proven to be challenging. Women in the region are underrepresented in STEM10 and even more so in leadership positions.17,18 The limited availability of current information and the difficulties associated with obtaining reliable data to mitigate gender disparities create difficulties in implementing policies to reduce the gender gap in STEM. Researchers, organizations, and policymakers working to reduce the gender gap need access to dependable data to understand the root causes of gender disparities, promote evidence-based interventions, and increase accountability and transparency. In the quest for solutions to these challenges, an international research network between Bolivia, Brazil, and Peru, "Equality in Leadership for Latin America STEM" (ELLAS), emerged in 2022.6

Motivation: Biological data may be separated into primary data, such as gene expression, and secondary data, such as pathways and protein-protein interactions. Methods using secondary data to enhance the analysis of primary data are promising, because secondary data have background information that is not included in primary data. In this study, we proposed an end-to-end framework to integrally handle secondary data to construct a classification model for primary data. We applied this framework to cancer prognosis prediction using gene expression data and a biological network. Results: Cross-validation results indicated that our model achieved higher accuracy compared with a deep neural network model without background biological network information. Experiments conducted in patient groups by cancer type showed improvement in ROC-area under the curve for many groups. Visualizations of high accuracy cancer types identified contributing genes and pathways by enrichment analysis. Known biomarkers and novel biomarker candidates were identified through these experiments.

Machine learning has opened up new tools for financial fraud detection. Using a sample of annotated transactions, a machine learning classification algorithm learns to detect frauds. With growing credit card transaction volumes and rising fraud percentages there is growing interest in finding appropriate machine learning classifiers for detection. However, fraud data sets are diverse and exhibit inconsistent characteristics. As a result, a model effective on a given data set is not guaranteed to perform on another. Further, the possibility of temporal drift in data patterns and characteristics over time is high. Additionally, fraud data has massive and varying imbalance. In this work, we evaluate sampling methods as a viable pre-processing mechanism to handle imbalance and propose a data-driven classifier selection strategy for characteristic highly imbalanced fraud detection data sets. The model derived based on our selection strategy surpasses peer models, whilst working in more realistic conditions, establishing the effectiveness of the strategy.

Credit cards play an exploding role in modern economies. Its popularity and ubiquity have created a fertile ground for fraud, assisted by the cross boarder reach and instantaneous confirmation. While transactions are growing, the fraud percentages are also on the rise as well as the true cost of a dollar fraud. Volume of transactions, uniqueness of frauds and ingenuity of the fraudster are main challenges in detecting frauds. The advent of machine learning, artificial intelligence and big data has opened up new tools in the fight against frauds. Given past transactions, a machine learning algorithm has the ability to 'learn' infinitely complex characteristics in order to identify frauds in real-time, surpassing the best human investigators. However, the developments in fraud detection algorithms has been challenging and slow due the massively unbalanced nature of fraud data, absence of benchmarks and standard evaluation metrics to identify better performing classifiers, lack of sharing and disclosure of research findings and the difficulties in getting access to confidential transaction data for research. This work investigates the properties of typical massively imbalanced fraud data sets, their availability, suitability for research use while exploring the widely varying nature of fraud distributions. Furthermore, we show how human annotation errors compound with machine classification errors. We also carry out experiments to determine the effect of PCA obfuscation (as a means of disseminating sensitive transaction data for research and machine learning) on algorithmic performance of classifiers and show that while PCA does not significantly degrade performance, care should be taken to use the appropriate principle component size (dimensions) to avoid overfitting.

The fundamental requirement for data collection is to encapsulate quality confirmation to answer all the question that have been established. Through data collection in medical devices Research and business analysis can work out quality content that is prerequisite for making well versed decisions. To develop the quality of content, data collection is a toolto help draw conclusions and make knowledgeable decisions based on the considered facts. To get solutions for their related questions and assess the results we need a data which is a systematic procedure of collecting and analyzing exact information.It mainly focuses on obtaining out comes of all there is to aexacting area under discussion. Data is collected to be further subjected to hypothesis testing which seek to explain a observable fact.

Why does a secondary data store matter for AI? In my previous blog in this data store series, I discussed how the real selection criteria for an AI/ML data platform is how to obtain the best balance between capacity (cost per GB stored) and performance (cost per GB of throughput). Indeed, to support enterprise AI programs, the data architecture must support both high performance (needed for Ai training and validation) and high capacity (needed to store the huge amount of data that AI training requires). Even if these two capabilities can be hosted on the same systems (integrated data platform) or in large infrastructures, they are hosted in two separated specialized systems (two-tier architecture). This post continues the series of blogs dedicated to data stores for AI and advanced analytics.

True distance memory-based heuristics (TDHs) were recently introduced as a way to obtain admissible heuristics for explicit state spaces. In this paper, we introduce a new TDH, the portal-based heuristic. The domain is partitioned into regions and portals between regions are identified. True distances between all pairs of portals are stored and used to obtain admissible heuristics throughout the search. We introduce an A*-based algorithm that takes advantage of the special properties of the new heuristic. We study the advantages and limitations of the new heuristic. Our experimental results show large performance improvements over previously-reported TDHs for commonly used classes of maps.

Pattern Databases (PDBs) are the most common form of memory-based heuristics, and they have been widely used in a variety of permutation puzzles and other domains. We explore the true-distance heuristics (TDHs) (also appeared in  (Sturtevant et al. 2009)) which are a different form of memory-based heuristics, designed to work in problem states where there isn't a fixed goal state. Unlike PDBs, which build a heuristic based on distances in an abstract state space, TDHs store distances which are computed in the actual state space. We look in detail at how TDHs work, providing both theoretical and experimental motivation for their use.