Uncertainty reports as explainable AI
Abstract As large language models (LLMs) increasingly support high-stakes human- AI decision-making , understanding how humans interpret LLM-generated uncertainty becomes critical. Existing explainable AI (XAI) methods for LLMs focus on post-hoc, model-centric explanations, often overlooking human cognitive responses and contextual demands.
This research examines uncertainties in AI -enabled decision-making applications and some approaches for managing various types of uncertainty . By referring to studies on uncertainty in decision making , this research describes three dimensions of uncertainty , namely informational, environmental and intentional.
Key Details About Uncertainty Modeling For Ai Decision-Making
Furthermore, visual representations like the one above help us fully grasp the concept of Uncertainty Modeling For AI Decision Making.
Abstract AI predictive systems are becoming integral to decision-making pipelines, shaping high-stakes choices once made solely by humans. Yet robust decisions under uncertainty still depend on capabilities that current AI lacks: domain knowledge not captured by data, long-horizon context, and the ability to reason and act in the physical world.
Abstract Introduction: Decision-making based on AI can be challenging, especially when considering the uncertainty associated with AI predictions. Visualizing uncertainty in AI refers to techniques that use visual cues to represent the level of confidence or uncertainty in an AI model's outputs, such as predictions or decisions . This study aims to investigate the impact of visualizing ...
Explainability through uncertainty
The paper reports the case of making a strategic decision in a state of AI -related uncertainty . It discusses the matter in the context of computational strategy, organizational resilience, and ...
Explaining the Uncertainty in AI
The aim of this project is to improve human decision-making using explainability; specifically, how to explain the ( un)certainty of machine learning models. Prior research has used uncertainty measures to promote trust and decision-making . However, the direction of explaining why the AI pre-diction is confident (or not confident) in its prediction needs to be addressed. By explaining the model ...
As we can see from the illustration, Uncertainty Modeling For AI Decision Making has many fascinating aspects to explore.
Uncertainty in AI is a fundamental challenge that affects decision-making , model reliability, and predictive accuracy. AI systems must operate in environments where data is incomplete, noisy, or ambiguous, making uncertainty management crucial for their effectiveness.
Explaining the Uncertainty in AI
The aim of this project is to improve human decision-making using explainability; specifically, how to explain the ( un)certainty of machine learning models. Prior research has used uncertainty measures to promote trust and decision-making . However, the direction of explaining why the AI prediction is confident (or not confident) in its prediction needs to be addressed. By explaining the model ...
