In settings where ML models are used to inform the allocation of resources, agents affected by the allocation decisions might have an incentive to strategically change their features to secure better outcomes. While prior work has studied strategic responses broadly, disentangling misreporting from genuine adaptation remains a fundamental challenge. In this paper, we propose a causally-motivated approach to identify and quantify how much an agent misreports on average by distinguishing deceptive changes in their features from genuine adaptation. Our key insight is that, unlike genuine adaptation, misreported features do not causally affect downstream variables (i.e., causal descendants). We exploit this asymmetry by comparing the causal effect of misreported features on their causal descendants as derived from manipulated datasets against those from unmanipulated datasets. We formally prove identifiability of the misreporting rate and characterize the variance of our estimator. We empirically validate our theoretical results using a semi-synthetic and real Medicare dataset with misreported data, demonstrating that our approach can be employed to identify misreporting in real-world scenarios.

For an LLM to correctly respond to an instruction it must understand both the semantics and the domain (i.e., subject area) of a given task-instruction pair. However, syntax can also convey implicit information. Recent work shows that syntactic templates--frequent sequences of Part-of-Speech (PoS) tags--are prevalent in training data and often appear in model outputs. In this work we characterize syntactic templates, domain, and semantics in task-instruction pairs. We identify cases of spurious correlations between syntax and domain, where models learn to associate a domain with syntax during training; this can sometimes override prompt semantics.

'There's no evidence that jobs go away, but there is a lot of evidence that as soon as you can dismantle full-time employment, companies will do that.' 'There's no evidence that jobs go away, but there is a lot of evidence that as soon as you can dismantle full-time employment, companies will do that.' Gig workers are endlessly exploited. As companies integrate AI and hire fewer employees, a shift toward a'gig economy' will commence The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link.

Recent studies have shown LLMs possess some ability to improve their responses when given external feedback. However, it remains unclear how effectively and thoroughly these models can incorporate extrinsic feedback. In an ideal scenario, if LLMs receive near-perfect and complete feedback, we would expect them to fully integrate the feedback and reach correct solutions. In this paper, we systematically investigate LLMs' ability to incorporate feedback by designing a controlled experimental environment. For each problem, a solver model attempts a solution, then a feedback generator with access to near-complete ground-truth answers produces targeted feedback, after which the solver tries again. We evaluate this pipeline across a diverse range of tasks, including math reasoning, knowledge reasoning, scientific reasoning, and general multi-domain evaluations with state-of-the-art language models including Claude 3.7 with extended thinking.

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.

Traditional Home Insurance Is Collapsing. A new, AI-assisted model of insurance is quietly exploding in disaster-prone areas--and may be coming for FEMA too. Is it the answer to climate change, or a trap? In 2019, when the worst flooding in recorded history spread across the entire Mississippi River basin, Colin Wellenkamp's phone rang for weeks. Wellenkamp runs a nonprofit called the Mississippi River Cities & Towns Initiative, which coordinates between mayors' offices in more than 100 river communities from northern Minnesota to southern Louisiana. As he describes it, his headquarters served as "one big virtual situation room" for relief agencies and municipalities up and down the central US.

Anthropic CEO Dario Amodei speaks during the World Economic Forum's annual meeting in Davos, Switzerland, in January. Electronics maker NEC said Thursday that major Japanese financial institutions, including Sumitomo Mitsui Financial Group and MS&AD Insurance Group Holdings, will participate in its strategic collaboration with U.S. startup Anthropic in the field of artificial intelligence. The partnership aims to improve the quality of financial services for customers using AI and to strengthen measures against cyberattacks. The other companies are Sumitomo Life Insurance, Daiwa Securities Group, Sumitomo Mitsui Trust Group, Sumitomo Mitsui Trust Bank and Meiji Yasuda Life Insurance. Using Anthropic's AI technology, the partners will work not only on developing new services but also on improving productivity by streamlining business processes at each company.

A panel of executives spoke at the TIME100 AI Leadership Forum on Wednesday night in New York City about the ways artificial intelligence is reshaping the business landscape, and how they're shepherding their companies into a technologically capricious future. Included on the panel at the TIME forum, which spotlighted AI-driven business leadership, were Nigel Vaz, the chief executive officer of Publicis Sapient, a tech-consulting firm that uses AI to help modernize business and a sponsor of Wednesday's event; Deepa Soni, the executive vice president and chief information officer of New York Life Insurance Company; and Ravi Radhakrishnan, the executive vice president and chief information officer of American Express. Vaz began the conversation discussing the "exponential" capability of AI to transform and enhance companies' abilities to problem solve and become more efficient. For his company, AI is a tool used to extract value and optimize performance for clients by reducing time and cost. Many of them, he notes, must bridge the gap between their relatively outdated technology and increasingly more useful AI tools--what he referred to as their "tech debt."

Traditional insurance pricing relies on risk-based principles that ensure actuarial fairness and solvency but do not explicitly account for policyholders' price sensitivity. We formulate insurance pricing as a decision-making problem and study it using tools from off-policy evaluation and stochastic control. We propose a kernelized inverse propensity score estimator that exploits local structure in the action space and yields variance reduction compared to the classical inverse propensity score estimator. Building on these value estimates, we investigate policy optimization and present two practical approaches for computing optimal pricing rules: an interpretable data-shared Lasso formulation and a flexible policy parameterization based on neural networks. Using a controlled synthetic travel insurance environment, we empirically confirm the theoretical results and show that neural networks outperform existing techniques for policy optimization.

Capped-usage SaaS products -- LLM subscriptions such as Claude Code and ChatGPT, cloud platforms such as Vercel and Cloudflare Workers, corporate benefit platforms, identity-verification services with liability transfer -- share a structural signature with insurance products: a fixed premium decoupled from realized consumption, stochastic per-user demand with heavy-tailed severity, a non-fungible cap that resets on a fixed schedule, and a portfolio-level exposure that requires reserve adequacy under tail risk. We argue that this is not an analogy. It is the same operational problem actuarial science has been tooled for decades to address, restated with new dependent variables (tokens, bandwidth bytes, function-invocations, gym check-ins) in place of medical claims. This paper proposes a modeling framework for capped-usage SaaS pricing built from frequency-severity decomposition, premium calculation principles, and Monte Carlo reserve adequacy. We map the framework to publicly observable subscription tiers in two domains (LLM services and cloud platforms), ground it in canonical health-insurance economics (Arrow 1963; Pauly 1968; Manning et al. 1987; Brot-Goldberg et al. 2017), and demonstrate divergence from traditional unit economics through a worked example. The contribution is operational rather than theoretical: not a new theorem, but vocabulary and tools currently absent from cs.LG/stat.ML practice.