Who Wins and Who Loses When AI Makes Decisions
What are the hidden risks and trade-offs in letting machines decide — and how can we protect fairness without stifling innovation?
US has slashed global vaccine funding – if philanthropy fills the gap, there could be some trade-offs
Bill Gates gives a baby in a woman's arms a rotavirus vaccine in Ghana in 2013. Pius Utomi Ekpei/AFP via Getty ImagesThe U.S.
How can you be sure your clothing has been produced ethically?
Naomi Rahim/Getty Images/CanvaToday’s consumers are swimming in a sea of information. Products are marketed with big, bold words such as “sustainable”, “ethical” and “organic”. They sound good, they catch our attention, and they make us feel better about what we buy.
The reality is, in today’s market, figuring out which claims are true is no easy task.
Mind the App: do European deposits react to digitalisation?
The March 2023 banking turmoil has intensified discussions whether social media and the digitalisation of finance have become significant factors in driving severe deposit outflows. We introduce the concept of deposits-at-risk and utilize quantile regressions for disentangling determinants of stressed outflows at the lowest tail of the distribution.
Higher-order exposures
Traditional exposure measures focus on direct exposures to evaluate the losses an institution is exposed to upon the default of a counterparty. Since the Global Financial Crisis of 2007-2008, the importance of indirect exposures via common asset holdings is increasingly recognized. Yet direct and indirect exposures do not to capture the losses that result from shock propagation and amplification following the counterparty's default. In this paper, we introduce the concept of \higher-order exposures" to refer to these spill-over losses and propose a way to formalize and quantify these.
FEDS Paper: Linear and nonlinear econometric models against machine learning models: realized volatility prediction
Rehim KilicThis paper fills an important gap in the volatility forecasting literature by comparing a broad suite of machine learning (ML) methods with both linear and nonlinear econometric models using high-frequency realized volatility (RV) data for the S&P 500. We evaluate ARFIMA, HAR, regime-switching HAR models (THAR, STHAR, MSHAR), and ML methods including Extreme Gradient Boosting, deep feed-forward neural networks, and recurrent networks (BRNN, LSTM, LSTM-A, GRU).
Bureau of Labor Statistics tells the US what’s up with the economy – Trump firing its top official may undercut trust in its data
Isador Lubin, chief of the Bureau of Labor Statistics, presents data to a Senate committee in 1937. Library of CongressMany financial and political analysts are trying to assess the impact of President Donald Trump’s decision to fire U.S. Bureau of Labor Statistics Commissioner Erika McEntarfer on Aug.
The ASX’s rookie error is just the latest of many blunders. Investors are losing confidence
It was the latest blow to the credibility of the Australian Securities Exchange (ASX). This time, the nation’s stock exchange mixed up two company names in an error that briefly wiped A$400 million off the market value of our third biggest telco, TPG Telecom.
Hulk Hogan and the unraveling of worker solidarity
Hulk Hogan was arguably WWE's biggest star in the 1980s. Wally McNamee/Corbis via Getty ImagesHulk Hogan’s death by heart attack at age 71 came as a shock to many fans of the larger-than-life wrestler who’d earned the nickname “The Immortal.”
Bank Rate reduced to 4% - August 2025
The Bank of England’s Monetary Policy Committee is responsible for making decisions about Bank Rate.
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