Consumer agency in the digital age is increasingly constrained by systemic barriers and algorithmic manipulation, raising concerns about the authenticity of consumption choices. Nowadays, financial decisions are shaped by external pressures like obligatory consumption, algorithmic persuasion, and unstable work schedules that erode financial autonomy. Obligatory consumption (like hidden fees) is intensified by digital ecosystems. Algorithmic tactics like personalized recommendations lead to impulsive purchases. Unstable work schedules also undermine financial planning. Thus, it is important to study how these factors impact consumption agency. To do so, we examine formal models grounded in discounted consumption with constraints that bound agency. We construct analytical scenarios in which consumers face obligatory payments, algorithm-influenced impulsive expenses, or unpredictable income due to temporal instability. Using this framework, we demonstrate that even rational, utility-maximizing agents can experience early financial ruin when agency is limited across structural, behavioral, or temporal dimensions and how diminished autonomy impacts long-term financial well-being. Our central argument is that consumer agency must be treated as a value (not a given) requiring active cultivation, especially in digital ecosystems. The connection between our formal modeling and this argument allows us to indicate that limitations on agency (whether structural, behavioral, or temporal) can be rigorously linked to measurable risks like financial instability. This connection is also a basis for normative claims about consumption as a value, by anchoring them in a formally grounded analysis of consumer behavior. As solutions, we study systemic interventions and consumer education to support value deliberation and informed choices. We formally demonstrate how these measures strengthen agency.

Would you like to work nearly double the standard 40-hour week? It's a question that many startups in the US are asking prospective employees--and to get the job, the answer needs to be an unequivocal yes. These companies are embracing an intense schedule, first popularized in mainland China, known as "996," or 9 am to 9 pm, six days a week. The 996 phenomenon in China gave rise to major protests and accusations of "modern slavery," with critics blaming the schedule for a spate of worker deaths. Despite the negative connotations overseas, US firms, many of them working on artificial intelligence, are adopting both the schedule and its nickname as they race to compete against each other--and with China.

The transition from traditional power grids to smart grids, significant increase in the use of renewable energy sources, and soaring electricity prices has triggered a digital transformation of the energy infrastructure that enables new, data driven, applications often supported by machine learning models. However, the majority of the developed machine learning models rely on univariate data. To date, a structured study considering the role meta-data and additional measurements resulting in multivariate data is missing. In this paper we propose a taxonomy that identifies and structures various types of data related to energy applications. The taxonomy can be used to guide application specific data model development for training machine learning models. Focusing on a household electricity forecasting application, we validate the e ff ectiveness of the proposed taxonomy in guiding the selection of the features for various types of models. Finally, using a feature importance techniques, we explain individual feature contributions to the forecasting accuracy.1. Introduction The transition from traditional power grids to smart grids, significant increase in the use of renewable energy sources, and soaring electricity prices has led to an increase in complexity [1], particularly with the adoption of smart meters (SMs), energy management systems (EMSes), and intelligent electronic devices (IEDs) at the low voltage (L V) level. These devices enable innovative energy [2] and non-energy applications [3, 4], such as energy cost optimization and matching consumption with self-production from renewable energy sources. On the distribution system operator (DSO) side of the L V grid, reliability and latency are the main challenges, and complete ob-servability of the L V grid for each substation is crucial.

Financial instability has become a significant issue in today's society. While research typically focuses on financial aspects, there is a tendency to overlook time-related aspects of unstable work schedules. The inability to rely on consistent work schedules leads to burnout, work-family conflicts, and financial shocks that directly impact workers' income and assets. Unforeseen fluctuations in earnings pose challenges in financial planning, affecting decisions on savings and spending and ultimately undermining individuals' long-term financial stability and well-being. This issue is particularly evident in sectors where workers experience frequently changing schedules without sufficient notice, including those in the food service and retail sectors, part-time and hourly workers, and individuals with lower incomes. These groups are already more financially vulnerable, and the unpredictable nature of their schedules exacerbates their financial fragility. Our objective is to understand how unforeseen fluctuations in earnings exacerbate financial fragility by investigating the extent to which individuals' financial management depends on their ability to anticipate and plan for the future. To address this question, we develop a simulation framework that models how individuals optimize utility amidst financial uncertainty and the imperative to avoid financial ruin. We employ online learning techniques, specifically adapting workers' consumption policies based on evolving information about their work schedules. With this framework, we show both theoretically and empirically how a worker's capacity to anticipate schedule changes enhances their long-term utility. Conversely, the inability to predict future events can worsen workers' instability. Moreover, our framework enables us to explore interventions to mitigate the problem of schedule uncertainty and evaluate their effectiveness.

Artificial intelligence (AI) can be a powerful tool to help build a more inclusive economy.ljubaphoto It's no secret that the pandemic resulted in women and marginalized communities being ousted from the work force in record numbers. Though many demographic sectors have since bounced back, the gains remain unequal among traditionally under-represented groups. For example, employment in the accommodation and food service industries, which are traditionally staffed primarily by women, are still 17 per cent below pre-pandemic levels. And while the unemployment rate for racialized workers has returned to pre-pandemic levels, it's still higher than that of non-racialized workers.

Retailers, banks, and many other companies collect and analyze information, realizing that data runs the Business. For business development, it is necessary to test hundreds of hypotheses through various methods, and here comes Data Science. Data science applies various big data tools and machine learning (ML), including algorithms and methods of artificial intelligence (AI). The task of ML is to "teach" a program to take appropriate actions based on the huge amount of processed data. Big data is the way of collecting, storing, processing and analyzing information.

Workers in India (89%) and China (88%) are more trusting of robots over their managers, followed by Singapore (83%), Brazil (78%), Japan (76%), UAE (74%), Australia/New Zealand (58%), the U.S. (57%), the U.K. (54%), and France (56%). More men (56%) than women (44%) have turned to AI over their managers.

Part of the point of artificial intelligence is to automate things so you can save time. It seems every week there is a new AI-powered app that makes some aspect of day-to-day life easier. For most, one of the biggest parts of day-to-day life is work, and there are fewer who love to save time more than busy professionals. Here are six ways you can harness the power of AI to save time throughout your workday, freeing you up for other tasks. Oftentimes the first big decision of the workday comes before you even leave your house -- what to wear.

Big data analysis has been used to improve the healthcare industry in several ways, such as developing personalized medicines, working to fight cancer and making pharmaceutical trials more efficient. Another exciting area that big data is ameliorating is the ability to forecast admission rates. With the help of machine learning, big data is able to predict the number of admissions a healthcare facility will have at any given time. This predicted number will allow for these facilities to better prepare for their anticipated number of patients by having enough staff ready to work, surgeries scheduled at the most opportune times, and the right type and amount of supplies stocked. This new application of big data analysis and machine learning to predict admission rates is based on both internal and external data.