(ZAC), the pioneer Cognitive Explainable-AI (Artificial Intelligence) (Cognitive XAI) software startup, has made AI and Machine Learning (ML) …

The worrying part being that the cheating software uses machine learning to see what other players are doing on the same network such as detecting …

In the past, hiring decisions were made by people. Today, some key decisions that lead to whether someone gets a job or not are made by algorithms. The use of AI-based job interviews has increased since the pandemic. As demand increases, so too do questions about whether these algorithms make fair and unbiased hiring decisions, or find the most qualified applicant. In this second episode of a four-part series on AI in hiring, we meet some of the big players making this technology including the CEOs of HireVue and myInterview--and we test some of these tools ourselves. This miniseries on hiring was reported by Hilke Schellmann and produced by Jennifer Strong, Emma Cillekens, Karen Hao and Anthony Green with special thanks to James Wall. Jennifer: Work… is a big part of our lives. It's how most of us pay our bills, feed our families… and put a roof over our heads. Michelle Rogers: "A permanent job would mean stability. You need something to keep you going and to keep you fresh." Dora Lespier: "Like being able to take my daughter being able to get whatever she needs. Henry Claypool: "You know, it's, it's a big part of my identity. It's what I do a lot.

This commentary aims to address the field of Artificial intelligence (AI) in Digital Pathology (DP) both in terms of the global situation and research …

"Great teams require great players and Dr. Peter Holden is a great player in the world of applying Artificial Intelligence (AI) and cognitive technologies …

Argonne National Laboratory researchers have used machine learning to rapidly optimize the application of thin films to semiconductors, a move that …

Though deep learning and AI have become household terms, the … focus on methods in statistics and machine learning, rather than equally important …

When insurance providers tap into the vast repositories of Big Data that is available to them and combine this data with machine learning and AI …

Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. A specific form of fairness is supplier exposure fairness where the objective is to ensure equitable coverage of items across all suppliers in recommendations provided to users. This is especially important in multistakeholder recommendation scenarios where it may be important to optimize utilities not just for the end-user, but also for other stakeholders such as item sellers or producers who desire a fair representation of their items. This type of supplier fairness is sometimes accomplished by attempting to increasing aggregate diversity in order to mitigate popularity bias and to improve the coverage of long-tail items in recommendations. In this paper, we introduce FairMatch, a general graph-based algorithm that works as a post processing approach after recommendation generation to improve exposure fairness for items and suppliers. The algorithm iteratively adds high quality items that have low visibility or items from suppliers with low exposure to the users' final recommendation lists. A comprehensive set of experiments on two datasets and comparison with state-of-the-art baselines show that FairMatch, while significantly improves exposure fairness and aggregate diversity, maintains an acceptable level of relevance of the recommendations.

They can now apply their AI program to real-world conditions as severe drought continues to plague parts of the Prairies and Great Plains states.