Generalisation of Total Uncertainty in AI: A Theoretical Study
This work addresses uncertainty in AI for decision-making and forecasting, but appears incremental as it builds on existing theories and methodologies.
The study tackled the problem of uncertainty in AI, particularly with small or varying datasets, by proposing a novel total uncertainty definition, aiming to improve understanding and handling of uncertainty across domains.
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.