As financial institutions increasingly rely on machine learning models to automate lending decisions, concerns about algorithmic fairness have risen. This paper explores the tradeoff between enforcing fairness constraints (such as demographic parity or equal opportunity) and maximizing lender profitability. Through simulations on synthetic data that reflects real-world lending patterns, we quantify how different fairness interventions impact profit margins and default rates. Our results demonstrate that equal opportunity constraints typically impose lower profit costs than demographic parity, but surprisingly, removing protected attributes from the model (fairness through unawareness) outperforms explicit fairness interventions in both fairness and profitability metrics. We further identify the specific economic conditions under which fair lending becomes profitable and analyze the feature-specific drivers of unfairness. These findings offer practical guidance for designing lending algorithms that balance ethical considerations with business objectives.

The mainstream media has much leeway in what it chooses to cover and how it covers it. These choices have real-world consequences on what people know and their subsequent behaviors. However, the lack of objective measures to evaluate editorial choices makes research in this area particularly difficult. In this paper, we argue that there are newsworthy topics where objective measures exist in the form of supporting data and propose a computational framework to analyze editorial choices in this setup. We focus on the economy because the reporting of economic indicators presents us with a relatively easy way to determine both the selection and framing of various publications. Their values provide a ground truth of how the economy is doing relative to how the publications choose to cover it. To do this, we define frame prediction as a set of interdependent tasks. At the article level, we learn to identify the reported stance towards the general state of the economy. Then, for every numerical quantity reported in the article, we learn to identify whether it corresponds to an economic indicator and whether it is being reported in a positive or negative way. To perform our analysis, we track six American publishers and each article that appeared in the top 10 slots of their landing page between 2015 and 2023.

Accurate demand forecasting in the retail industry is a critical determinant of financial performance and supply chain efficiency. As global markets become increasingly interconnected, businesses are turning towards advanced prediction models to gain a competitive edge. However, existing literature mostly focuses on historical sales data and ignores the vital influence of macroeconomic conditions on consumer spending behavior. In this study, we bridge this gap by enriching time series data of customer demand with macroeconomic variables, such as the Consumer Price Index (CPI), Index of Consumer Sentiment (ICS), and unemployment rates. Leveraging this comprehensive dataset, we develop and compare various regression and machine learning models to predict retail demand accurately.

Moatasem Jibril, a young man from Sudan, is realising his dream of conducting technological experiments to manufacture robots by using recycled electronic waste. Despite modest capabilities and living in a mud house in the city of Omdurman, west of the capital, Khartoum, Jibril did not give up on his dream of making a robot, even after having to quit university due to the deteriorating economic conditions of his family. For about ten years, Jibril has been trying to create robots in a narrow space inside his family house, and he challenges poverty by working daily in the market to earn money to purchase the materials he needs for his project. He hopes that his dream will be funded by any businessman or institution. Sudan is suffering from many crises, starting with a shortage of basic and imported commodities, as well as the depreciation of the local currency, in addition to the government's measures to lift fuel subsidies at the request of the International Monetary Fund in 2021.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Some of the biggest tech names have laid off artificial intelligence (AI) and machine learning (ML) employees this fall, including Meta, Twitter and Amazon. In light of that, it would make sense for industry nerves to be high entering 2023, but that's not the case. Even in the midst of a possible recession, AI experts across several industries told VentureBeat that they expect AI innovation to continue and companies to adjust budgets and priorities accordingly. In fact, these industry leaders resoundingly underscored three areas where AI has thrived over the past year and will continue to grow in 2023: workplace automation and human-centric AI; data-driven AI decision-making; and generative AI use cases.

Artificial intelligence (AI) and machine learning (ML) are ubiquitous in consumers' lives, from the "up next" suggestions from your streaming service to routes suggested by your GPS when you plug an address into your phone for directions. Less visible impacts of AI and ML include the use of AI to control data center efficiency and cooling or the management of restaurant wait times, as some companies use AI to make decisions about how many burgers to cook for the day's lunch rush. Whereas AI refers to the ability of a computer to emulate human decision-making, ML is the algorithm-driven foundation that enables AI. We can think of automation as the application of AI to develop a series of repeatable tasks or actions designed to accomplish a certain task or execute a process. Companies use automation for transporting products to warehouse workers for packing, processing invoices, and assisting with many other repetitive business tasks that humans have historically performed.

COMSovereign Holding Corp. (NASDAQ: COMS) ("COMSovereign" or "Company"), a U.S.-based developer of 4G LTE Advanced and 5G Communication Systems and Solutions, today announced that it has executed an agreement to acquire RVision, Inc. ("RVision"), a developer of technologically advanced, environmentally hardened video and communications products and physical security solutions designed for government and private sector commercial industries. Terms of the transaction include total consideration of approximately $5.58 million consisting exclusively of shares of restricted common stock. The transaction is expected to close within approximately 15 days subject to traditional closing conditions. Smart Cities and Smart Campuses (educational and industrial) are urban areas designed to integrate advanced technologies including IoT ("Internet of Things"), AI ("Artificial Intelligence"), machine learning, Big Data, and sustainable or "green" energy systems to benefit and secure the daily lives of its residents. Around the world today, these technologies are being deployed to efficiently improve public services and safety through enhancements to everything from mass transportation and waste management to the real-time monitoring of environmental conditions including air and water quality.

Steven Spielberg's new blockbuster, Ready Player One, is the most significant Hollywood depiction of gamer culture to date. For the first time in mainstream cinema, it presents video games not merely as the cliched subcultural world of geeks and nerds, but as a significant force shaping the future of entertainment, communication, love, and politics. In this way, it does justice to the importance of video games, which have an increasing role in social and cultural life. As such, the celebrated director is showing the worst side of gamer culture. The movie adapts Ernest Cline's 2012 novel, in which "most of the human race spend all of their free time inside a video game" powerful enough to transform "entertainment, social networking and even global politics".

Satellites are best known for helping smartphones map driving routes or televisions deliver programs. But now, data from some of the thousands of satellites orbiting Earth are helping track things like crop conditions on rural farms, illegal deforestation, and increasingly, poverty in the hard-to-reach places around the globe. As much as that data has the potential to provide invaluable information to humanitarian organizations, watchdog groups, and policymakers, there is too much of it to sift through in order to draw insights that could influence important decisions. A team of researchers from Stanford University, however, says it has developed an efficient way. By creating a deep-learning algorithm that can recognize signs of poverty in satellite images – such as condition of roads – the team sorted through a million images to accurately identify economic conditions in five African countries, reported the scientists in the journal Science on Thursday.

Satellites are best known for helping smartphones map driving routes or televisions deliver programs. But now, data from some of the thousands of satellites orbiting Earth are helping track things like crop conditions on rural farms, illegal deforestation, and increasingly, poverty in the hard-to-reach places around the globe. As much as that data has the potential to provide invaluable information to humanitarian organizations, watchdog groups, and policymakers, there is too much of it to sift through in order to draw insights that could influence important decisions. A team of researchers from Stanford University, however, says it has developed an efficient way. By creating a deep-learning algorithm that can recognize signs of poverty in satellite images – such as condition of roads – the team sorted through a million images to accurately identify economic conditions in five African countries, reported the scientists in the journal Science on Thursday.