The risk for lead exposure is disproportionately higher for children who are poor, non-Hispanic black, living in large metropolitan areas, or living in older housing. The CDC sets a national standard for blood lead levels in children. This value was established in 2012 to be 3.5 micrograms per decileter (µg/dL) of blood.

Data distillation and coresets have emerged as popular approaches to generate a smaller representative set of samples for downstream learning tasks to handle large-scale datasets. At the same time, machine learning is being increasingly applied to decision-making processes at a societal level, making it imperative for modelers to address inherent biases towards subgroups present in the data. While current approaches focus on creating fair synthetic representative samples by optimizing local properties relative to the original samples, their impact on downstream learning processes has yet to be explored. In this work, we present fair Wasserstein coresets (FWC), a novel coreset approach which generates fair synthetic representative samples along with sample-level weights to be used in downstream learning tasks. FWC uses an efficient majority minimization algorithm to minimize the Wasserstein distance between the original dataset and the weighted synthetic samples while enforcing demographic parity. We show that an unconstrained version of FWC is equivalent to Lloyd's algorithm for k-medians and k-means clustering. Experiments conducted on both synthetic and real datasets show that FWC: (i) achieves a competitive fairness-utility tradeoff in downstream models compared to existing approaches, (ii) improves downstream fairness when added to the existing training data and (iii) can be used to reduce biases in predictions from large language models (GPT-3.5 and GPT-4).

The risk for lead exposure is disproportionately higher for children who are poor, non-Hispanic black, living in large metropolitan areas, or living in older housing. The CDC sets a national standard for blood lead levels in children. This value was established in 2012 to be 3.5 micrograms per decileter (µg/dL) of blood.

Although large language models (LLMs) have shown great performance in natural language processing (NLP) in the financial domain, there are no publicly available financially tailored LLMs, instruction tuning datasets, and evaluation benchmarks, which is critical for continually pushing forward the open-source development of financial artificial intelligence (AI). This paper introduces PIXIU, a comprehensive framework including the first financial LLM based on fine-tuning LLaMA with instruction data, the first instruction data with 128K data samples to support the fine-tuning, and an evaluation benchmark with 8 tasks and 15 datasets. We first construct the large-scale multi-task instruction data considering a variety of financial tasks, financial document types, and financial data modalities. We then propose a financial LLM called FinMA by fine-tuning LLaMA with the constructed dataset to be able to follow instructions for various financial tasks. To support the evaluation of financial LLMs, we propose a standardized benchmark that covers a set of critical financial tasks, including six financial NLP tasks and two financial prediction tasks. With this benchmark, we conduct a detailed analysis of FinMA and several existing LLMs, uncovering their strengths and weaknesses in handling critical financial tasks.

AI has transformed everyday tasks such as writing, coding -- even shopping. Now, Visa is introducing an initiative to prepare its payment network for a new era of agentic AI shopping experiences. Earlier this week, the company unveiled Visa Intelligent Commerce at the Visa Global Product Drop. According to the release, this initiative opens Visa's payment network to developers and engineers who are building agentic AI shopping experiences that find and buy products for users. Also: Google's AI Mode may be the upgrade Search desperately needs - how to try it for free Moreover, Visa Intelligent Commerce is a commercial partner program for AI platforms that includes a suite of integrated APIs developers can use to deploy Visa's AI commerce capabilities.

AI has transformed everyday tasks such as writing, coding -- even shopping. Now, Visa is introducing an initiative to prepare its payment network for a new era of agentic AI shopping experiences. On Wednesday, the company unveiled Visa Intelligent Commerce at the Visa Global Product Drop. According to the release, this initiative opens Visa's payment network to developers and engineers who are building agentic AI shopping experiences that find and buy products for users. Moreover, Visa Intelligent Commerce is a commercial partner program for AI platforms that includes a suite of integrated APIs developers can use to deploy Visa's AI commerce capabilities.

Travel expert Colleen Kelly shares the hottest travel destinations for this summer and provides tips for travelers planning a cruise. As you rush through busy terminals, juggling bags and boarding passes, your credit cards may be at risk, not just from pickpockets, but from digital thieves using high-tech tools like RFID (radio-frequency identification) skimmers. While today's chip-enabled cards are more secure than old magnetic stripes, it's still wise to take extra precautions, especially in crowded places like airports. Here's how to keep your cards protected while traveling. GET SECURITY ALERTS & EXPERT TECH TIPS – SIGN UP FOR KURT'S'THE CYBERGUY REPORT' NOW WHAT IS ARTIFICIAL INTELLIGENCE (AI)?

Fairness studies of algorithmic decision-making systems often simplify complex decision processes, such as bail or loan approvals, into binary classification tasks. However, these approaches overlook that such decisions are not inherently binary (e.g., approve or not approve bail or loan); they also involve non-binary treatment decisions (e.g., bail conditions or loan terms) that can influence the downstream outcomes (e.g., loan repayment or reoffending). In this paper, we argue that non-binary treatment decisions are integral to the decision process and controlled by decision-makers and, therefore, should be central to fairness analyses in algorithmic decision-making. We propose a causal framework that extends fairness analyses and explicitly distinguishes between decision-subjects' covariates and the treatment decisions. This specification allows decision-makers to use our framework to (i) measure treatment disparity and its downstream effects in historical data and, using counterfactual reasoning, (ii) mitigate the impact of past unfair treatment decisions when automating decision-making. We use our framework to empirically analyze four widely used loan approval datasets to reveal potential disparity in non-binary treatment decisions and their discriminatory impact on outcomes, highlighting the need to incorporate treatment decisions in fairness assessments. Moreover, by intervening in treatment decisions, we show that our framework effectively mitigates treatment discrimination from historical data to ensure fair risk score estimation and (non-binary) decision-making processes that benefit all stakeholders.