Inspectorio, creators of an AI-powered supplier performance management platform, has expanded Inspectorio Rise, an all-in-one supply chain sustainability and compliance solution. Inspectorio Rise provides brands, retailers and suppliers with a centralized platform to make production more efficient, transparent and beneficial for people and the planet. As the world witnesses an increasing number of environmental and social regulations -- from the European Due Diligence Directive to the California Transparency in Supply Chains Act -- global companies need to adapt to the new context, maintaining compliance and scaling sustainability across the supply chain. Inspectorio Rise helps manage and streamline due diligence and reporting processes, and provides insights to make evidence-based decisions in the face of new and existing business challenges. Inspectorio Rise allows brands, retailers and suppliers to manage their end-to-end sustainability, compliance and responsible sourcing activities.

Machine learning (ML) is playing an increasingly important role in rendering decisions that affect a broad range of groups in society. ML models inform decisions in criminal justice, the extension of credit in banking, and the hiring practices of corporations. This posits the requirement of model fairness, which holds that automated decisions should be equitable with respect to protected features (e.g., gender, race, or age) that are often under-represented in the data. We postulate that this problem of under-representation has a corollary to the problem of imbalanced data learning. This class imbalance is often reflected in both classes and protected features. For example, one class (those receiving credit) may be over-represented with respect to another class (those not receiving credit) and a particular group (females) may be under-represented with respect to another group (males). A key element in achieving algorithmic fairness with respect to protected groups is the simultaneous reduction of class and protected group imbalance in the underlying training data, which facilitates increases in both model accuracy and fairness. We discuss the importance of bridging imbalanced learning and group fairness by showing how key concepts in these fields overlap and complement each other; and propose a novel oversampling algorithm, Fair Oversampling, that addresses both skewed class distributions and protected features. Our method: (i) can be used as an efficient pre-processing algorithm for standard ML algorithms to jointly address imbalance and group equity; and (ii) can be combined with fairness-aware learning algorithms to improve their robustness to varying levels of class imbalance. Additionally, we take a step toward bridging the gap between fairness and imbalanced learning with a new metric, Fair Utility, that combines balanced accuracy with fairness.

Technological advances in omics evaluation, bioinformatics, and artificial intelligence have made us rethink ways to improve patient outcomes. Collective quantification and characterization of biological data including genomics, epigenomics, metabolomics, and proteomics is now feasible at low cost with rapid turnover. Significant advances in the integration methods of these multiomics data sets by machine learning promise us a holistic view of disease pathogenesis and yield biomarkers for disease diagnosis and prognosis. Using machine learning tools and algorithms, it is possible to integrate multiomics data with clinical information to develop predictive models that identify risk before the condition is clinically apparent, thus facilitating early interventions to improve the health trajectories of the patients. In this review, we intend to update the readers on the recent developments related to the use of artificial intelligence in integrating multiomic and clinical data sets in the field of perinatology, focusing on neonatal intensive care and the opportunities for precision medicine.

"I was a happy data scientist until we decided it was time for deploying our models." It is common among many DS/ML teams that when the time for productionizing the model comes, they are caught off guard due to poor planning. Of course, thinking solely about the end is far from enough, the stages beforehand are equally as important. To reach the end of any endeavor we need to be strategic, the same applies for succeeding with MLOps. One such strategy is to take on a less challenging problem or part of it in the beginning and find the easiest way it can be solved.

Abbreviation is Artificial Intelligence on a digital globe background. A power plant that will run on "artificial intelligence" is about to get underway in West Africa. The joint venture between Swiss-based Xcell Security House and Finance and U.S.-based Beyond Limits will embed intelligence and awareness into the operations -- something that will create more efficiencies, greater productivity, and increased environmental protections. When ordinary people hear about artificial intelligence -- AI for short -- they immediately think about how machines will replace humans. But as the experts explained to this reporter, AI is meant to eliminate "mundane activities" so that those running heavy industrial operations can solve problems and improve performance, which translates into healthier bottom lines.

Implementing AI across an organization requires a clear strategy, organizational flexibility, a data-driven environment and new forms of collaboration with cross-functional teams.

Abbreviation is Artificial Intelligence on a digital globe background. A power plant that will run on "artificial intelligence" is about to get underway in West Africa. The joint venture between Swiss-based Xcell Security House and Finance and U.S.-based Beyond Limits will embed intelligence and awareness into the operations -- something that will create more efficiencies, greater productivity, and increased environmental protections. When ordinary people hear about artificial intelligence -- AI for short -- they immediately think about how machines will replace humans. But as the experts explained to this reporter, AI is meant to eliminate "mundane activities" so that those running heavy industrial operations can solve problems and improve performance, which translates into healthier bottom lines.

Every day, businesses and organizations are tasked with making more decisions than any human could ever hope to handle. Often, enterprises need to make complex business decisions with limited information on hand. With the help of AI-based products and AI-driven enterprise search solutions as a critical enabling technology, leaders can make better strategic and informed decisions by gaining insight from a vast amount of data in a short period. With the assistance of custom dashboards and 360-degree views of data, employees with different roles can each have a single view into all the information they need at its most appropriate level. The key message for companies is that making the right decisions and fast decisions are not either-or anymore.

Best papers of the workshop, after further revisions and independent reviews, will be considered for publication in a special issue of a renowned journal. By this holistic view we encounter a variety of challenges along the AI modeling cycle and software system engineering lifecycle as outlined in the figure below such as: • theory-practice gap in machine learning with impact on stability, reproducibility or integrity due to limitations of nowadays theoretical foundations in statistical learning theory or lack of control of high-dimensionality effects of deep learning; • facing computational constraints, e.g. All submissions will be peer-reviewed by, at least, 3 reviewers and judged on the basis of originality, contribution to the field, technical and presentation quality, and relevance to the workshop. Short papers are meant for timely discussion and feedback at the workshop. Papers are accepted with the understanding that at least one author will register for the conference to present the paper.

A common marketing analytics challenge is to understand consumer behavior and develop customer attributes or archetypes. As organizations get better at tackling this problem, they can activate marketing strategies to incorporate additional customer knowledge into their campaigns. Building customer profiles is now easier than ever with BigQuery ML, using a technique called clustering. In this post, you'll learn how to create segmentation and how to use these audiences for marketing activation. Clustering algorithms can group similar user behavior together to build segmentation used for marketing.