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This paper presents a novel data-driven approach to mitigating employee attrition using machine learning and data engineering techniques. The proposed framework integrates data from various human resources systems and leverages advanced feature engineering to capture a comprehensive set of factors influencing attrition. The study outlines a robust modeling approach that addresses challenges such as imbalanced datasets, categorical data handling, and model interpretation. The methodology includes careful consideration of training and testing strategies, baseline model establishment, and the development of calibrated predictive models. The research emphasizes the importance of model interpretation using techniques like SHAP values to provide actionable insights for organizations. Key design choices in algorithm selection, hyperparameter tuning, and probability calibration are discussed. This approach enables organizations to proactively identify attrition risks and develop targeted retention strategies, ultimately redu

This paper presents a theoretical framework for an AI-driven data quality monitoring system designed to address the challenges of maintaining data quality in high-volume environments. We examine the limitations of traditional methods in managing the scale, velocity, and variety of big data and propose a conceptual approach leveraging advanced machine learning techniques. Our framework outlines a system architecture that incorporates anomaly detection, classification, and predictive analytics for real-time, scalable data quality management. Key components include an intelligent data ingestion layer, adaptive preprocessing mechanisms, context-aware feature extraction, and AI-based quality assessment modules. A continuous learning paradigm is central to our framework, ensuring adaptability to evolving data patterns and quality requirements. We also address implications for scalability, privacy, and integration within existing data ecosystems. While practical results are not provided, it lays a robust theoretical foundation for future research and implementations, advancing data quality management and encouraging the exploration of AI-driven solutions in dynamic environments.

Medicaid is a federal-state program that provides healthcare to over 80 million low-income Americans, including pregnant women, children, and individuals with disabilities. Up against a host of problems, including rising healthcare costs, disparity in access, and the management of chronic conditions among at-risk groups, Medicaid is one of the biggest healthcare payers in the U.S. Just as Medicare does, the use of Artificial Intelligence (AI) offers a major opportunity to change the delivery of care and operational efficiency in Medicaid [1] [16]. While there has been extensive conversation about AI in Medicare, the unique population and requirements of Medicaid require customized AI applications [1]. Chronic disease management, improving admin tasks, and a reduction in costs are amongst the ways AI tools can help, especially by focusing on social determinants of health (SDOH) that are important for Medicaid populations. The study will assess the ability of AI-enabled systems to reinforce Medicaid in handling its particular challenges while facilitating fair and quality care for its entire population of beneficiaries [8] [9].

Common problems in playing online mobile and computer games were related to toxic behavior and abusive communication among players. Based on different reports and studies, the study also discusses the impact of online hate speech and toxicity on players' in-game performance and overall well-being. This study investigates the capability of pre-trained language models to classify or detect trash talk or toxic in-game messages The study employs and evaluates the performance of pre-trained BERT and GPT language models in detecting toxicity within in-game chats. Using publicly available APIs, in-game chat data from DOTA 2 game matches were collected, processed, reviewed, and labeled as non-toxic, mild (toxicity), and toxic. The study was able to collect around two thousand in-game chats to train and test BERT (Base-uncased), BERT (Large-uncased), and GPT-3 models. Based on the three models' state-of-the-art performance, this study concludes pre-trained language models' promising potential for addressing online hate speech and in-game insulting trash talk.

Fox News correspondent David Spunt has the latest on questions over whether the former president can hold office again on Special Report. A liberal reporter added fuel to online fire that a conservative news outlet was duped by a former President Trump impersonator, or even artificial intelligence – resulting in an embarrassing editor's note. Last week, Trump called into right-wing channel Real America's Voice for an interview that resulted in online speculation that the outlet had spoken with an impostor. Audio was shaky, and speculation erupted that Trump either had a cold, poor service or something more malicious, such as someone impersonating the 45th president, or modern technology generating the interview with old clips of Trump. Zachary Petrizzo, a politics reporter for the left-wing Daily Beast, took things a step further and reported that Real America's Voice owner Robert Sigg told him that the company would investigate whether the call was some sort of prank.

If you want to spend a little less on a deluxe robot vacuum, today is the day. Amazon is selling the iRobot Roomba S9 a self-emptying station and the iRobot Braava Jet m6 bundle for $999. When we reviewed the S9 back in 2021, we gave it 4.5 out of 5 stars and an Editors' Choice Award. "The S9 is worth every penny," we said. It's also our pick as the most sophisticated robot vacuum you can get in our very own 2022 roundup. We also reviewed the iRobot Braava Jet m6 and gave it an equally powerful 4.5 out of 5 stars plus an Editors' Choice Award.

Author summary Artificial Intelligence (AI) creates opportunities for accurate, objective and immediate decision support in healthcare with little expert input–especially valuable in resource-poor settings where there is shortage of specialist care. Given that AI poorly generalises to cohorts outside those whose data was used to train and validate the algorithms, populations in data-rich regions stand to benefit substantially more vs data-poor regions, entrenching existing healthcare disparities. Here, we show that more than half of the datasets used for clinical AI originate from either the US or China. In addition, the U.S. and China contribute over 40% of the authors of the publications. While the models may perform on-par/better than clinician decision-making in the well-represented regions, benefits elsewhere are not guaranteed. Further, we show discrepancies in gender and specialty representation–notably that almost three-quarters of the coveted first/senior authorship positions were held by men, and radiology accounted for 40% of all clinical AI manuscripts. We emphasize that building equitable sociodemographic representation in data repositories, in author nationality, gender and expertise, and in clinical specialties is crucial in ameliorating health inequities.

In her 2018 book "Double Negative: The Black Image and Popular Culture," Racquel Gates explores the disruptive potential of stereotypical or so-called negative images of Black people onscreen: Flavor Flav on VH1's "Flavor of Love," for example, and the stars of "ratchet" reality shows such as "Basketball Wives." These images, Gates argues, intervene against narratives of racial uplift that are overly tethered to white and middle-class definitions of respectability. In her acknowledgments section, Gates, a professor of film and media studies at Columbia, invokes a scene from "Love & Hip Hop," in which an aspiring singer tells an entertainment manager, "I want to be on your roster." Gates writes, "While I was tempted to quote this bit of dialogue to my editor, Ken Wissoker, during our first meeting, I erred on the side of caution." Wissoker, who has been an editor at Duke University Press since 1991, has a formidable roster, and one could easily imagine a reality show about junior scholars fighting for a chance to work with him.

What does the crystal ball portend for AI as we are halfway through the first business quarter of the year? First, of course, we already know that artificial intelligence (AI) impacts every industry on the planet. Here are some areas in which AI will play a more significant role in our lives in 2022 and beyond. AI feasts on data and the gathering avenues of that information have heightened the value of data as a competitive advantage and a critical asset for businesses and governments alike. As a result, privacy regulations have been enacted and initiatives to educate the public about how their data can be used. Individuals will have more agency in exercising their data rights due to these efforts.