Personalization usually gets a big plus in many contexts. Think about many potential axes, including language, geographic location, task orientation, product/service description, medical condition, garment size, food allergies, educational focus, job category, news preference: The list is long. The consequences of this kind of personalization are usually seen as useful because the system is intended to produce results tailored to an individual's interests. In the advertising world, this is often extremely valuable since the information is targeted at a specific need or interest. The same can be said for many other specific cases in which a need or interest is satisfied more effectively.

No, SNAP Benefits Aren't Mostly Used by Immigrants SNAP benefits are set to run out on Saturday. Far-right influencers and extremists are incorrectly claiming that immigrants are the main recipients of food stamps. A shopper carries a basket inside a grocery store in the Bronx borough of New York City on Oct. 24, 2025. As roughly 42 million Americans face the loss of food stamps this weekend, far-right influencers, extremists, and conspiracy theorists are using the crisis to push racist disinformation about who receives these benefits. As a result of the government shutdown, the Supplemental Nutrition Assistance Program (SNAP) will not be funded as of November 1, according to a message on the website of the US Department of Agriculture (USDA), which administers the program.

Meat reduction benefits human and planetary health, but social norms keep meat central in shared meals. To date, the development of communication strategies that promote meat reduction while minimizing social costs has required the costly involvement of human participants at each stage of the process. We present work in progress on simulating multi-round dialogues on meat reduction between Generative Agents based on large language models (LLMs). We measure our main outcome using established psychological questionnaires based on the Theory of Planned Behavior and additionally investigate Social Costs. We find evidence that our preliminary simulations produce outcomes that are (i) consistent with theoretical expectations; and (ii) valid when compared to data from previous studies with human participants. Generative agent-based models are a promising tool for identifying novel communication strategies on meat reduction -- tailored to highly specific participant groups -- to then be tested in subsequent studies with human participants.

The flowchart of our cohort definition protocol is illustrated in Figure 6.

In addition to this matching distribution constraint, we also require that the recipient's

Regression-based predictive analytics used in modern kidney transplantation is known to inherit biases from training data. This leads to social discrimination and inefficient organ utilization, particularly in the context of a few social groups. Despite this concern, there is limited research on fairness in regression and its impact on organ utilization and placement. This paper introduces three novel divergence-based group fairness notions: ( i) independence, ( ii) separation, and ( iii) sufficiency to assess the fairness of regression-based analytics tools. In addition, fairness preferences are investigated from crowd feedback, in order to identify a socially accepted group fairness criterion for evaluating these tools. A total of 85 participants were recruited from the Prolific crowdsourcing platform, and a Mixed-Logit discrete choice model was used to model fairness feedback and estimate social fairness preferences. The findings clearly depict a strong preference towards the separation and sufficiency fairness notions, and that the predictive analytics is deemed fair with respect to gender and race groups, but unfair in terms of age groups.

This study investigates the subjective experiences of users in two robotic object delivery methods: direct handover and table placement, when users are occupied with another task. A user study involving 15 participants engaged in a typing game revealed that table placement significantly enhances user experience compared to direct handovers, particularly in terms of satisfaction, perceived safety and intuitiveness. Additionally, handovers negatively impacted typing performance, while all participants expressed a clear preference for table placement as the delivery method. These findings highlight the advantages of table placement in scenarios requiring minimal user disruption.

The Association for Computing Machinery, has named Andrew Barto and Richard Sutton as the recipients of the 2024 ACM A.M. Turing Award. The pair have received the honour for "developing the conceptual and algorithmic foundations of reinforcement learning". In a series of papers beginning in the 1980s, Barto and Sutton introduced the main ideas, constructed the mathematical foundations, and developed important algorithms for reinforcement learning. The Turing Award comes with a 1 million prize, to be split between the recipients. Since its inception in 1966, the award has honoured computer scientists and engineers on a yearly basis.

Knowledge representation and reasoning systems represent knowledge as collections of facts and rules. KRRs can represent complex concepts and relations, and they can query and manipulate information in sophisticated ways. Unfortunately, the KRR technology has been hindered by the fact that specifying the requisite knowledge requires skills that most domain experts do not have, and professional knowledge engineers are hard to find. Some recent CNL-based approaches, such as the Knowledge Authoring Logic Machine (KALM), have shown to have very high accuracy compared to others, and a natural question is to what extent the CNL restrictions can be lifted. Besides the CNL restrictions, KALM has limitations in terms of the types of knowledge it can represent. To address these issues, we propose an extension of KALM called KALM for Factual Language (KALMF). KALMF uses a neural parser for natural language, MS, to parse what we call factual English sentences, which require little grammar training to use. Building upon KALMF, we propose KALM for Rules and Actions (KALMR), to represent and reason with rules and actions. Furthermore, we identify the reasons behind the slow speed of KALM and make optimizations to address this issue. Our evaluation using multiple benchmarks shows that our approaches achieve a high level of correctness on fact and query authoring (95%) and on rule authoring (100%). When used for authoring and reasoning with actions, our approach achieves more than 99.3% correctness, demonstrating its effectiveness in enabling more sophisticated knowledge representation and reasoning. We also illustrate the logical reasoning capabilities of our approach by drawing attention to the problems faced by the famous AI, ChatGPT. Finally, the evaluation of the newly proposed speed optimization points not only to a 68% runtime improvement but also yields better accuracy of the overall system.