01-29-2026
From healthcare and finance to online experimentation and personalized platforms, AI-driven decision systems are now embedded in high-stakes environments. Many of these systems rely on reinforcement learning or “bandit” algorithms that adapt their behavior over time to maximize performance metrics such as clicks, revenue or accuracy. While this focus on optimization has delivered impressive gains, new research highlights a critical blind spot: performance alone is not enough. Leaders also need to know what the system has learned and how confident it is in those conclusions.
Daniels School professors Will Wei Sun and Yichen Zhang from the Quantitative Methods Department and PhD student Qiyu Han highlight this gap in a paper published in The Annals of Statistics, “Online Statistical Inference In Decision-Making With Matrix Context.” Their research shows how to conduct statistically valid inference — reliable statements about uncertainty, confidence intervals and hypothesis tests — while an AI system is learning and making decisions in real time. The results carry important implications for executives overseeing AI deployment, experimentation and governance, allowing them to provide agile, evidence-based data analysis that costs less.
Most adaptive AI systems are designed to answer one question: Which action performs best right now? In many settings, however, leaders care about deeper insights. For example, in healthcare or product experimentation, it matters not only which option wins, but whether the evidence is strong enough to justify a change.
The paper shows that it is possible — and necessary — to quantify uncertainty in sequential decision systems. Statistical inference allows organizations to answer questions like:
Without such tools, AI systems risk becoming overconfident, making decisions that look optimal in the short run but lack reliable evidence.
A core challenge is that adaptive systems collect data in a non-random way. When A/B testing bandit algorithms, future decisions depend on past outcomes. This violates a key assumption behind standard statistical methods, such as ordinary least squares regression.
The research shows that applying classical tools in these settings can produce misleading confidence intervals and hypothesis tests, often making results appear more certain than they really are. For managers, this means that familiar analytics dashboards may silently overstate evidence when data are collected adaptively. The paper develops new inference methods specifically designed to remain valid even when data collection depends on past decisions, reducing the risk of false discoveries and premature conclusions.
Modern AI systems often rely on rich, high-dimensional information: images, user-item interaction matrices or detailed feedback signals. While these datasets are large, they often contain hidden structure. One common example is low-rank structure, meaning that outcomes are driven by a relatively small number of underlying factors rather than every variable independently.
The proposed methods dramatically improve sample efficiency, learning more from less data. Importantly, the approach works without storing massive historical datasets, which reduces storage costs and improves scalability. For organizations operating real-time platforms, this translates into faster learning, lower infrastructure demands and more interpretable models.
A key practical contribution is that inference is performed fully online. Estimation and uncertainty assessment are updated on the fly, without pausing experiments, splitting data or revisiting old samples. This makes the methods especially suitable for real-time systems such as recommendation engines, adaptive pricing or automated experimentation platforms.
For leaders, this means uncertainty can be monitored continuously, not just after an experiment ends. Decisions can be adjusted dynamically as confidence grows, supporting more agile and evidence-based management.
Although the paper focuses on bandit algorithms, its implications extend to emerging challenges in AI alignment and reinforcement learning from human feedback, techniques increasingly used to train and evaluate large language models. In these settings, understanding not just what the system predicts but how confident it is becomes essential for safe and responsible deployment.
Human-in-the-loop systems depend on knowing when an AI model is uncertain and when human oversight is most valuable. By integrating valid statistical inference into adaptive learning, organizations can design AI systems that are not only effective, but also transparent, auditable and aligned with human judgment.
For managers and leaders, the clarion call is here. As AI systems become more adaptive and autonomous, success depends not just on optimizing outcomes, but on building decision systems that can explain what they know, quantify what they don’t and support confident, responsible action in real time.
