The International Monetary Fund (IMF) has a responsibility to help poorer nations struggling with rising debt, high inflation and price shocks caused by war spillovers, the head of the global lender says. Speaking as the IMF's annual meetings in Bangkok wrapped up on Saturday, Managing Director Kristalina Georgieva said discussions with global policymakers showed both "optimism and worry". "But AI is also recognised to be a source of worry and so are inflation, high levels of debt, wars, climate risks." Georgieva said vulnerable nations are missing out on the AI investment boom that is driving growth in richer countries while at the same time dealing with higher borrowing costs. Higher global interest rates mean low-income countries now spend 8 to 14 percent of their revenue on debt repayments, up from previous years, she said.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Mortgage lenders probed over how they handled L.A. wildfire insurance payouts Altadena residents and council members gather at a fire-damaged residential property on East Las Flores Drive in support of Senate Bill 1090 on June 29, 2026, in Altadena, CA. Two thirds of Altadena homes were destroyed in the Eaton fire on January 7, 2025. SB 1090 would help protect fire survivors while preserving opportunities for affordable housing. This is read by an automated voice.

International Monetary Fund Managing Director Kristalina Georgieva speaks during the IMF/World Bank 2026 Annual Meetings in Bangkok on Monday. The head of the International Monetary Fund has warned of the disruptive threat from artificial intelligence to the labor market and taxation, urging policymakers to rethink education and act now to help society adapt. "It is a slow-moving tsunami, but tsunami none the less," Managing Director Kristalina Georgieva said Monday during her first appearance at the annual meetings of the IMF and World Bank in Bangkok. Speaking at a session on AI's impact on employment, she said the technology is already hollowing out demand for many human workers by taking over routine, repetitive tasks. At the same time, it is increasing low-paying jobs in sectors like construction, which she said is "not a story to celebrate."

The average interest rate on a new five-year fixed mortgage deal has hit 6% for the first time in three years, figures show. The cost of home loans has been rising in recent weeks, as lenders face higher costs amid international concern over rising prices, interest rates, and government borrowing costs. It means home buyers and anyone renewing a fixed deal have seen about 1,500 mortgage deals priced below 5% vanish since the start of September, according to the financial information service Moneyfacts. It described the situation as brutal for borrowers, with the average rate on five-year deals now at 6%, and at 5.98% on two-year fixed mortgages. For borrowers, the interest rate on a fixed mortgage does not change until it expires, usually after two or five years, and a new one is chosen to replace it.

What's happening to UK interest rates and what does it mean for mortgages? The Bank of England has kept UK interest rates unchanged at 3.75% for a sixth time in a row, keeping them at the lowest level since February 2023. Before the US-Israeli war with Iran, rates had been expected to fall in 2026, but the economic fallout from the conflict has pushed up inflation across the globe, making cuts unlikely and rises a possibility. Interest rates affect mortgage, credit card and savings rates for millions of people. What are interest rates and why do they change?

UK average mortgage rates have risen back to the level of a month ago as renewed tensions in the Middle East feed through to homeowners. Lenders' funding costs have increased as markets judge that a prolonged conflict reduces the possibility of interest rate cuts by central banks. The five biggest High Street banks are among a host of lenders which have increased their interest rates on new fixed deals in recent days. Recent projections by the Bank of England suggest just over five million homeowners should expect their monthly mortgage repayments to increase by the end of 2028. Mortgage rates had been falling as a ceasefire between the US and Iran initially appeared to hold.

Uncertainty estimates in machine learning are typically evaluated using generic metrics such as the negative log-likelihood and expected calibration error, yet good performance on such metrics does not necessarily imply high utility in downstream decisions. We introduce decision-alignment, a criterion that reveals which evaluation metrics meaningfully align with downstream utilities. Applying this framework, we show that many widely used uncertainty metrics are either misaligned with common decision problems or encode pathological prior beliefs about the downstream task. We then propose prior-weighted utility metrics, a special class of proper scoring rules that provides decision-aligned uncertainty evaluation. Across benchmark experiments and real-world case studies, our metrics consistently align with realized decision utility, while conventional metrics do not. Our results surface flaws in the current UQ evaluation protocol and offer a principled extension of existing metrics toward decision-relevant UQ evaluation.

We introduce the zero-one censored transformed normal (ZOC-TN) model for proportional responses with potential probability mass at the boundaries 0 and 1. The model combines a censored Gaussian variable with a two-parameter affine-logit transformation on the interior (0,1). We characterize the transformation parameters, establish large-sample properties, and relate the affine-logit specification to broader classes of interior distributions. Theoretical and experimental results demonstrate that the proposed model can capture a wider range of qualitative density shapes than several benchmark models while remaining parsimonious, computationally efficient, and numerically stable. Furthermore, the ZOC-TN model can be extended (i) to account for nonlinearities and interactions in a tree-boosting machine learning framework and (ii) to explicitly model residual spatio-temporal variability. We apply the ZOC-TN model to loss given default (LGD) modeling for a large dataset of U.S. residential mortgages and compare it to multiple benchmark models. We find that a tree-boosted ZOC-TN model with a spatio-temporal frailty Gaussian process delivers the strongest out-of-sample performance, indicating that mortgage losses are shaped by nonlinear covariate effects and by unaccounted-for space-time variation.

Counterfactual explanations play a pivotal role in explainable artificial intelligence (XAI) by offering intuitive, human-understandable alternatives that elucidate machine learning model decisions. Despite their significance, existing methods for generating counterfactuals often require constant access to the predictive model, involve computationally intensive optimization for each instance and lack the flexibility to adapt to new user-defined constraints without retraining. In this paper, we propose DiCoFlex, a novel model-agnostic, conditional generative framework that produces multiple diverse counterfactuals in a single forward pass. Leveraging conditional normalizing flows trained solely on labeled data, DiCoFlex addresses key limitations by enabling real-time user-driven customization of constraints such as sparsity and actionability at inference time. Extensive experiments on standard benchmark datasets show that DiCoFlex outperforms existing methods in terms of validity, diversity, proximity, and constraint adherence, making it a practical and scalable solution for counterfactual generation in sensitive decision-making domains.

Algorithmic fairness has emerged as a central issue in ML, and it has become standard practice to adjust ML algorithms so that they will satisfy fairness requirements such as Equal Opportunity. In this paper we consider the effects of adopting such fair classifiers on the overall level of ecosystem fairness. Specifically, we introduce the study of fairness with competing firms, and demonstrate the failure of fair classifiers in yielding fair ecosystems. Our results quantify the loss of fairness in systems, under a variety of conditions, based on classifiers' correlation and the level of their data overlap. We show that even if competing classifiers are individually fair, the ecosystem's outcome may be unfair; and that adjusting biased algorithms to improve their individual fairness may lead to an overall decline in ecosystem fairness. In addition to these theoretical results, we also provide supporting experimental evidence. Together, our model and results provide a novel and essential call for action.