Generalisation of Total Uncertainty in AI: A Theoretical Study

TL;DR

This paper provides a comprehensive theoretical analysis of total uncertainty in AI, proposing a new unified definition to improve understanding and management of uncertainty across various applications.

Contribution

It introduces a novel total uncertainty definition in AI, integrating theories and methodologies to better understand and handle uncertainty.

Findings

Proposes a new total uncertainty framework for AI.

Provides an integrated view of uncertainty across domains.

Enhances understanding of uncertainty's role in AI decision-making.

Abstract

AI has been dealing with uncertainty to have highly accurate results. This becomes even worse with reasonably small data sets or a variation in the data sets. This has far-reaching effects on decision-making, forecasting and learning mechanisms. This study seeks to unpack the nature of uncertainty that exists within AI by drawing ideas from established works, the latest developments and practical applications and provide a novel total uncertainty definition in AI. From inception theories up to current methodologies, this paper provides an integrated view of dealing with better total uncertainty as well as complexities of uncertainty in AI that help us understand its meaning and value across different domains.