UK share values'most stretched' since 2008, Bank warns The Bank of England has warned of a sharp correction in the value of major tech companies with growing fears of an artificial intelligence (AI) bubble. It said share prices in the UK are close to the most stretched they have been since the 2008 global financial crisis, while equity valuations in the US are reminiscent of those before the dotcom bubble burst. The central bank's financial stability report warned valuations are particularly stretched for companies focused on AI. It said the growth of the sector in the next five years would be fuelled by trillions of dollars of debt, raising financial stability risks if the value of the companies falls. The Bank of England cited industry figures forecasting spending on AI infrastructure could top $5tn (£3.8tn).

We introduce a novel machine learning model for credit risk by combining tree-boosting with a latent spatio-temporal Gaussian process model accounting for frailty correlation. This allows for modeling non-linearities and interactions among predictor variables in a flexible data-driven manner and for accounting for spatio-temporal variation that is not explained by observable predictor variables. We also show how estimation and prediction can be done in a computationally efficient manner. In an application to a large U.S. mortgage credit risk data set, we find that both predictive default probabilities for individual loans and predictive loan portfolio loss distributions obtained with our novel approach are more accurate compared to conventional independent linear hazard models and also linear spatio-temporal models. Using interpretability tools for machine learning models, we find that the likely reasons for this outperformance are strong interaction and non-linear effects in the predictor variables and the presence of large spatio-temporal frailty effects.

Credit scores are critical for allocating consumer debt in the United States, yet little evidence is available on their performance. We benchmark a widely used credit score against a machine learning model of consumer default and find significant misclassification of borrowers, especially those with low scores. Our model improves predictive accuracy for young, low-income, and minority groups due to its superior performance with low quality data, resulting in a gain in standing for these populations. Our findings suggest that improving credit scoring performance could lead to more equitable access to credit.

Fairness in decision-making processes is often quantified using probabilistic metrics. However, these metrics may not fully capture the real-world consequences of unfairness. In this article, we adopt a utility-based approach to more accurately measure the real-world impacts of decision-making process. In particular, we show that if the concept of $\varepsilon$-fairness is employed, it can possibly lead to outcomes that are maximally unfair in the real-world context. Additionally, we address the common issue of unavailable data on false negatives by proposing a reduced setting that still captures essential fairness considerations. We illustrate our findings with two real-world examples: college admissions and credit risk assessment. Our analysis reveals that while traditional probability-based evaluations might suggest fairness, a utility-based approach uncovers the necessary actions to truly achieve equality. For instance, in the college admission case, we find that enhancing completion rates is crucial for ensuring fairness. Summarizing, this paper highlights the importance of considering the real-world context when evaluating fairness.

We propose an approach for the structure controllable summarization of long legal opinions that considers the argument structure of the document. Our approach involves using predicted argument role information to guide the model in generating coherent summaries that follow a provided structure pattern. We demonstrate the effectiveness of our approach on a dataset of legal opinions and show that it outperforms several strong baselines with respect to ROUGE, BERTScore, and structure similarity.

"With the amount of data today, we know there is no way we as human beings can process it all…The only technique we know that can harvest insight from the data, is artificial intelligence," IBM CEO Arvind Krishna recently told the Wall Street Journal. The insights to which Krishna is referring are patterns in the data that can help companies make predictions, whether that's the likelihood of someone defaulting on a mortgage, the probability of developing diabetes within the next two years, or whether a job candidate is a good fit. More specifically, AI identifies mathematical patterns found in thousands of variables and the relations among those variables. These patterns can be so complex that they can defy human understanding. This can create a problem: While we understand the variables we put into the AI (mortgage applications, medical histories, resumes) and understand the outputs (approved for the loan, has diabetes, worthy of an interview), we might not understand what's going on between the inputs and the outputs.

It started with a single tweet in November 2019. David Heinemeier Hansson, a high-profile tech entrepreneur, lashed out at Apple's newly launched credit card, calling it "sexist" for offering his wife a credit limit 20 times lower than his own. The allegations spread like wildfire, with Hansson stressing that artificial intelligence – now widely used to make lending decisions – was to blame. "It does not matter what the intent of individual Apple reps are, it matters what THE ALGORITHM they've placed their complete faith in does. And what it does is discriminate. While Apple and its underwriters Goldman Sachs were ultimately cleared by US regulators of violating fair lending rules last year, it rekindled a wider debate around AI use across public and private industries. Politicians in the European Union are now planning to introduce the first comprehensive global template for regulating AI, as institutions increasingly automate routine tasks in an attempt to boost efficiency and ...

The housing market is facing a very different future with the advent of machine learning. Learn how we used machine learning to predict the prices of homes in Toronto. Real estate is a billion-dollar industry but comes with many inefficiencies. Soon, realtors will be replaced by technology for their role as intermediaries between buyers and sellers. New innovations are changing how we approach real estate and it is for the better for ordinary people looking to buy or sell their first place.

Can common business sense be programmed into AI? Many are certainly trying to do just that. But there are decisions that often require a level of empathy -- let alone common-sense -- that may be too difficult to embed into algorithms. In addition, while AI and machine learning are the hot tickets of the moment, technologists and decision-makers need to think about whether it offers a practical solution to every problem or opportunity. Machine learning, task automation and robotics are already widely used in business.

Humans invented artificial intelligence, so it is an unfortunate reality that human biases can be baked into AI. Businesses that use AI, however, do not need to replicate these historical mistakes. Today, we can deploy and scale carefully designed AI across organizations to root out bias rather than reinforce it. This shift is happening now in consumer lending, an industry with a history of using biased systems and processes to write loans. For years, creditors have used models that misrepresent the creditworthiness of women and minorities with discriminatory credit-scoring systems and other practices. Until recently, for example, consistently paying rent did not help on mortgage applications, an exclusion that especially disadvantaged people of color.