As machine learning increasingly influences critical domains such as credit underwriting, public policy, and talent acquisition, ensuring compliance with fairness constraints is both a legal and ethical imperative. This paper introduces a novel framework for fairness adjustments that applies to diverse machine learning tasks, including regression and classification, and accommodates a wide range of fairness metrics. Unlike traditional approaches categorized as pre-processing, in-processing, or post-processing, our method adapts in-processing techniques for use as a post-processing step. By decoupling fairness adjustments from the model training process, our framework preserves model performance on average while enabling greater flexibility in model development. Key advantages include eliminating the need for custom loss functions, enabling fairness tuning using different datasets, accommodating proprietary models as black-box systems, and providing interpretable insights into the fairness adjustments. We demonstrate the effectiveness of this approach by comparing it to Adversarial Debiasing, showing that our framework achieves a comparable fairness/accuracy tradeoff on real-world datasets.

Data assimilation (DA) enables hydrologic models to update their internal states using near-real-time observations for more accurate forecasts. With deep neural networks like long short-term memory (LSTM), using either lagged observations as inputs (called "data integration") or variational DA has shown success in improving forecasts. However, it is unclear which methods are performant or optimal for physics-informed machine learning ("differentiable") models, which represent only a small amount of physically-meaningful states while using deep networks to supply parameters or missing processes. Here we developed variational DA methods for differentiable models, including optimizing adjusters for just precipitation data, just model internal hydrological states, or both. Our results demonstrated that differentiable streamflow models using the CAMELS dataset can benefit strongly and equivalently from variational DA as LSTM, with one-day lead time median Nash-Sutcliffe efficiency (NSE) elevated from 0.75 to 0.82. The resulting forecast matched or outperformed LSTM with DA in the eastern, northwestern, and central Great Plains regions of the conterminous United States. Both precipitation and state adjusters were needed to achieve these results, with the latter being substantially more effective on its own, and the former adding moderate benefits for high flows. Our DA framework does not need systematic training data and could serve as a practical DA scheme for whole river networks.

In anytime-valid sequential inference, it is known that any admissible inference procedure must be based on test martingales and their composite generalization, called e-processes, which are nonnegative processes whose expectation at any arbitrary stopping time is upper-bounded by one. An e-process quantifies the accumulated evidence against a composite null hypothesis over a sequence of outcomes. This paper studies methods for combining e-processes that are computed using different information sets, i.e., filtrations, for a null hypothesis. Even though e-processes constructed on the same filtration can be combined effortlessly (e.g., by averaging), e-processes constructed on different filtrations cannot be combined as easily because their validity in a coarser filtration does not translate to validity in a finer filtration. We discuss three concrete examples of such e-processes in the literature: exchangeability tests, independence tests, and tests for evaluating and comparing forecasts with lags. Our main result establishes that these e-processes can be lifted into any finer filtration using adjusters, which are functions that allow betting on the running maximum of the accumulated wealth (thereby insuring against the loss of evidence). We also develop randomized adjusters that can improve the power of the resulting sequential inference procedure.

Claims adjusters make decisions every day--million-dollar decisions that have the potential to change a claimant's life. If anyone needs a second set of eyes--that helpful colleague with tons of experience and sharp attention to detail--it's claims adjusters. Here's the thing: even two of the best claims adjusters with 60 years of combined experience probably haven't seen everything (although they may be pretty close). Every day there are new cases and unseen factors that offer data about the best course for a particular claim. That's where augmented intelligence comes in.

It's natural to link technology and artificial intelligence to industries like telecommunications, marketing, and manufacturing. Clients still receive cards in the mail, meet with agents in their offices, and speak with adjusters for claims. Yet technology is transforming the way insurance carriers provide coverage and how policyholders receive service. Technological advancements are starting to automate and predict standard insurance-related tasks, from filing a claim to adjusting a policy's coverage. As the industry embraces things like artificial intelligence, machine learning, and other technologies, the relationship between providers and clients is also changing.

For a forthcoming book with Nitin Mittal of Deloitte, I've been researching companies that are "All In on AI," as the book will be titled. These are companies who have made substantial and long-term bets on the notion that AI will revolutionize the way they do business. In several of the companies, the CEOs have been heavily engaged in the AI-driven transformation process. One of them is Piyush Gupta, the CEO of Singapore-based DBS Bank, whom I have written about elsewhere as an AI leader. Another is Peter Ma, the founder and chairman of Ping An, which is the largest private-sector firm in China.

For a forthcoming book with Nitin Mittal of Deloitte, I've been researching companies that are "All In on AI," as the book will be titled. These are companies who have made substantial and long-term bets on the notion that AI will revolutionize the way they do business. In several of the companies, the CEOs have been heavily engaged in the AI-driven transformation process. One of them is Piyush Gupta, the CEO of Singapore-based DBS Bank, whom I have written about elsewhere as an AI leader. Another is Peter Ma, the founder and chairman of Ping An, which is the largest private-sector firm in China. Ping An makes extensive use of AI to drive its five ecosystems, and my sources tell me that Ma is heavily involved in the decisions around the technology.

The AI revolution was – is – supposed to accomplish all this, and more. But it turns out that the revolution is more of an evolution, especially in traditional sectors like manufacturing, maintenance and insurance, where physical objects play a key role. AI has had an important impact on numerous industries, but it has yet to reach its full potential – either because the technology is still under development, or businesses are not quite ready for it. The commercial progress and success of AI has indeed been slower than many expected - but that "delay" can actually work in favor of business. As machine learning, neural networks, and other AI technologies improve, more businesses will be implementing them – and that gives businesses time to get in on the ground floor of technology that is already making itself felt – and will make itself felt even more in the coming years.

The algorithm will be used in tandem with the company's access to ultra-high-resolution imagery. The technology was previously used by the Brit claims team and its delegated claims adjusters in the wake of Hurricane Ida. Brit has successfully deployed the technology to expedite the identification of insured property damage in the wake of the tornadoes that ripped through the Midwest Dec. 10-11. The machine-learning algorithm, developed by the company's data science team, assesses ultra-high-resolution aerial images and data. The algorithm allows Brit's claims team to identify, triage and assign response activity even before claims are reported.

Organizations hoping to deploy artificial intelligence have to know what problems they're solving -- no vague questions allowed. Artificial intelligence (AI) and machine learning have come a long way, both in terms of adoption across the broader technology landscape and in the insurance industry specifically. That said, there is still much more territory to cover, helping integral employees like claims adjusters do their jobs better, faster and easier. Data science is currently being used to uncover insights that claims representatives wouldn't have found otherwise, which can be extremely valuable. Data science steps in to identify patterns within massive amounts of data that are too large for humans to comprehend on their own; machines can alert users to relevant, actionable insights that improve claim outcomes and facilitate operational efficiency.